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208  <header class="post-header">
209    <h1 class="post-title">Publications</h1>
210    <p class="post-description">I hope you enjoy reading my publications!</p>
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214    <!-- _pages/publications.md -->
215<div class="publications">
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217<h2 class="bibliography">2026</h2>
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250  <div id="zhang2026orion" class="col-sm-8">
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252    <div class="title">ORION: Option-Regularized Deep Reinforcement Learning for Cooperative Multi-Agent Online Navigation</div>
253    <!-- Author -->
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255      
256
257      
258      Shizhe
259            Zhang, Jingsong
260            Liang, Zhitao
261            Zhou, Shuhan
262            Ye, Yizhuo
263            Wang, Derek Ming Siang
264            Tan, Jimmy
265            Chiun, and
266        <span class="more-authors" title="click to view 2 more authors" onclick="
267              var element = $(this);
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269              var more_authors_text = element.text() == '2 more authors' ? 'Yuhong Cao, Guillaume Sartoretti' : '2 more authors';
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293      <em>IEEE Robotics and Automation Letters</em>,  2026
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317        <a href="https://drive.google.com/file/d/1Qp9IXjEZurAvdBfSAm2AeNoiBVhM_EFI/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
318      
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321        <a href="https://github.com/marmotlab/ORION" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
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339        <p>Existing methods for multi-agent navigation typically assume fully known environments, offering limited support for partially known scenarios with outdated or imperfect prior maps, such as warehouses or factory floors. There, agents need to balance path optimality with collecting and sharing environmental information to help teammates reach their own targets. To these ends, we propose ORION, a novel deep reinforcement learning framework for cooperative multi-agent online navigation in partially known environments. Starting from an imperfect prior map, ORION trains agents to make decentralized decisions, coordinate toward individual targets, and actively reduce task-relevant map uncertainty through online observation sharing in a closed perception—action loop. We first design a shared graph encoder that fuses prior map with online perception into a unified representation, providing robust state embeddings under environmental discrepancies. At the core of ORION is an option-critic framework that learns high-level cooperative modes translated into sequences of low-level actions, enabling adaptive switching between individual navigation and team-level exploration. We further introduce a dual-stage cooperation strategy that allows agents to assist teammates under map uncertainty, thereby reducing the overall makespan. Across extensive maze-like maps and large-scale warehouse environments, ORION achieves high-quality real-time decentralized cooperation while scaling to up to 10 robots, outperforming state-of-the-art classical and learning-based baselines. Finally, we validate ORION on physical robot teams, demonstrating its robustness and practicality for real-world cooperative navigation.</p>
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346        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">zhang2026orion</span><span class="p">,</span>
347  <span class="na">title</span> <span class="p">=</span> <span class="s">{ORION: Option-Regularized Deep Reinforcement Learning for Cooperative Multi-Agent Online Navigation}</span><span class="p">,</span>
348  <span class="na">author</span> <span class="p">=</span> <span class="s">{Zhang, Shizhe and Liang, Jingsong and Zhou, Zhitao and Ye, Shuhan and Wang, Yizhuo and Tan, Derek Ming Siang and Chiun, Jimmy and Cao, Yuhong and Sartoretti, Guillaume}</span><span class="p">,</span>
349  <span class="na">
349journal</span> <span class="p">=</span> <span class="s">{IEEE Robotics and Automation Letters}</span><span class="p">,</span>
350  <span class="na">year</span> <span class="p">=</span> <span class="s">{2026}</span><span class="p">,</span>
351  <span class="na">publisher</span> <span class="p">=</span> <span class="s">{IEEE}</span><span class="p">,</span>
352<span class="p">}</span></code></pre></figure>
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391  <div id="chen2026imaginav" class="col-sm-8">
392    <!-- Title -->
393    <div class="title">ImagiNav: Scalable Embodied Navigation via Generative Visual Prediction and Inverse Dynamics</div>
394    <!-- Author -->
395    <div class="author">
396      
397
398      
399      Jie
400            Chen, Yuxin
401            Cai, Yizhuo
402            Wang, Ruofei
403            Bai, <em>Yuhong
404            Cao</em>, Jun
405            Li, Yau Wei
406            Yun, and
407        <span class="more-authors" title="click to view 1 more author" onclick="
408              var element = $(this);
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418          ">1 more author</span>
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434      <em>2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)</em>,  2026
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453          <a href="https://arxiv.org/pdf/2603.13833" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
454        
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458        <a href="https://drive.google.com/file/d/1Q6WXRKYvgH_OBwIZXz0rde4BnagzBclz/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
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478        <p>Enabling robots to navigate open-world environments via natural language is critical for general-purpose autonomy. Yet, Vision-Language Navigation has relied on end-to-end policies trained on expensive, embodiment-specific robot data. While recent foundation models trained on vast simulation data show promise, the challenge of scaling and generalizing due to the limited scene diversity and visual fidelity in simulation persists. To address this gap, we propose ImagiNav, a novel modular paradigm that decouples visual planning from robot actuation, enabling the direct utilization of diverse in-the-wild navigation videos. Our framework operates as a hierarchy: a Vision-Language Model first decomposes instructions into textual subgoals; a finetuned generative video model then imagines the future video trajectory towards that subgoal; finally, an inverse dynamics model extracts the trajectory from the imagined video, which can then be tracked via a low-level controller. We additionally develop a scalable data pipeline of in-the-wild navigation videos auto-labeled via inverse dynamics and a pretrained Vision-Language Model. ImagiNav demonstrates strong zero-shot transfer to robot navigation without requiring robot demonstrations, paving the way for generalist robots that learn navigation directly from unlabeled, open-world data.</p>
479      </div>
480    
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484      <div class="bibtex hidden">
485        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">chen2026imaginav</span><span class="p">,</span>
486  <span class="na">title</span> <span class="p">=</span> <span class="s">{ImagiNav: Scalable Embodied Navigation via Generative Visual Prediction and Inverse Dynamics}</span><span class="p">,</span>
487  <span class="na">author</span> <span class="p">=</span> <span class="s">{Chen, Jie and Cai, Yuxin and Wang, Yizhuo and Bai, Ruofei and Cao, Yuhong and Li, Jun and Yun, Yau Wei and Sartoretti, Guillaume}</span><span class="p">,</span>
488  <span class="na">
488journal</span> <span class="p">=</span> <span class="s">{2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}</span><span class="p">,</span>
489  <span class="na">year</span> <span class="p">=</span> <span class="s">{2026}</span><span class="p">,</span>
490  <span class="na">webiste</span> <span class="p">=</span> <span class="s">{https://j1dan.github.io/ImagiNav/}</span>
491<span class="p">}</span></code></pre></figure>
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516      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/kivi-480.webp 480w,/assets/img/publication_preview/kivi-800.webp 800w,/assets/img/publication_preview/kivi-1400.webp 1400w," sizes="200px" type="image/webp"></source>
517    
518    <img src="/assets/img/publication_preview/kivi.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="kivi.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
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529  <!-- Entry bib key -->
530  <div id="li2025kivi" class="col-sm-8">
531    <!-- Title -->
532    <div class="title">KiVi: Kinesthetic-Visuospatial Integration for Dynamic and Safe Egocentric 
532Legged Locomotion</div>
533    <!-- Author -->
534    <div class="author">
535      
536
537      
538      Peizhuo
539            Li, Hongyi
540            Li, Yuxuan
541            Ma, Linnan
542            Chang, Xinrong
543            Yang, Ruiqi
544            Yu, Yifeng
545            Zhang, and
546        <span class="more-authors" title="click to view 3 more authors" onclick="
547              var element = $(this);
548              element.attr('title', '');
549              var more_authors_text = element.text() == '3 more authors' ? 'Yuhong Cao, Qiuguo Zhu, Guillaume Sartoretti' : '3 more authors';
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557          ">3 more authors</span>
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573      <em>2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)</em>,  2026
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587        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
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592          <a href="https://www.arxiv.org/pdf/2509.23650" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
593        
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597        <a href="https://drive.google.com/file/d/1Oh5L6opqgko-5RTwq9Yz6vBt7WTxIxF6/view?usp=drivesdk" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
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617        <p>Vision-based locomotion has shown great promise in enabling legged robots to perceive and adapt to complex environments. However, visual information is inherently fragile, being vulnerable to occlusions, reflections, and lighting changes, which often cause instability in locomotion. Inspired by animal sensorimotor integration, we propose KiVi, a Kinesthetic-Visuospatial integration framework, where kinesthetics encodes proprioceptive sensing of body motion and visuospatial reasoning captures visual perception of surrounding terrain. Specifically, KiVi separates these pathways, leveraging proprioception as a stable backbone while selectively incorporating vision for terrain awareness and obstacle avoidance. This modality-balanced, yet integrative design, combined with memory-enhanced attention, allows the robot to robustly interpret visual cues while maintaining fallback stability through proprioception. Extensive experiments show that our method enables quadruped robots to stably traverse diverse terrains and operate reliably in unstructured outdoor environments, remaining robust to out-of-distribution (OOD) visual noise and occlusion unseen during training, thereby highlighting its effectiveness and applicability to real-world legged locomotion.</p>
618      </div>
619    
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624        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">li2025kivi</span><span class="p">,</span>
625  <span class="na">title</span> <span class="p">=</span> <span class="s">{KiVi: Kinesthetic-Visuospatial Integration for Dynamic and Safe Egocentric 
625Legged Locomotion}</span><span class="p">,</span>
626  <span class="na">author</span> <span class="p">=</span> <span class="s">{Li, Peizhuo and Li, Hongyi and Ma, Yuxuan and Chang, Linnan and Yang, Xinrong and Yu, Ruiqi and Zhang, Yifeng and Cao, Yuhong and Zhu, Qiuguo and Sartoretti, Guillaume}</span><span class="p">,</span>
627  <span class="na">journal</span> <span class="p">=</span> <span class="s">{2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}</span><span class="p">,</span>
628  <span class="na">year</span> <span class="p">=</span> <span class="s">{2026}</span>
629<span class="p">}</span></code></pre></figure>
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636</li>
637<li>
638<div class="row">
639  
640    <div class="col col-sm-2 abbr">
641      
642        
643          
644          
645
646<figure>
647  <picture>
648    <!-- Auto scaling with imagemagick -->
649    <!--
650      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
651      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
652    -->
653    
654      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/apex-480.webp 480w,/assets/img/publication_preview/apex-800.webp 800w,/assets/img/publication_preview/apex-1400.webp 1400w," sizes="200px" type="image/webp"></source>
655    
656    <img src="/assets/img/publication_preview/apex.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="apex.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
657  </picture>
658
659  
660</figure>
661
662        
663      
664    </div>
665  
666
667  <!-- Entry bib key -->
668  <div id="sood2025apex" class="col-sm-8">
669    <!-- Title -->
670    <div class="title">APEX: Action Priors Enable Efficient Exploration for Skill Imitation on Articulated Robots</div>
671    <!-- Author -->
672    <div class="author">
673      
674
675      
676      Shivam
677            Sood, Laukik
678            Nakhwa, Ge
679            Sun, <em>Yuhong
680            Cao</em>, Jin
681            Cheng, Fatemah
682            Zargarbashi, Taerim
683            Yoon, and
684        <span class="more-authors" title="click to view 3 more authors" onclick="
685              var element = $(this);
686              element.attr('title', '');
687              var more_authors_text = element.text() == '3 more authors' ? 'Sungjoon Choi, Stelian Coros, Guillaume Sartoretti' : '3 more authors';
688              var cursorPosition = 0;
689              var textAdder = setInterval(function(){
690                element.text(more_authors_text.substring(0, cursorPosition + 1));
691                if (++cursorPosition == more_authors_text.length){
692                  clearInterval(textAdder);
693                }
694            }, '10');
695          ">3 more authors</span>
696      
697    </div>
698
699    <!-- Journal/Book title and date -->
700    
701    
702    
703    
704    
705    
706    
707    
708    
709    
710    <div class="periodical">
711      <em>2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)</em>,  2026
712    </div>
713    <div class="periodical">
714      
715    </div>
716
717    <!-- Links/Buttons -->
718    <div class="links">
719      
720      
721        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
722      
723      
724      
725        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
726      
727      
728      
729        
730          <a href="https://www.arxiv.org/pdf/2505.10022" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
731        
732      
733      
734      
735        <a href="https://drive.google.com/file/d/1vY5M1HS7L5etdv09ygXzTuavgFsa1Qzm/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
736      
737      
738      
739      
740      
741      
742        <a href="https://marmotlab.github.io/kakashi/" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Website</a>
743      
744    </div>
745    
746      
747      
748      
749      
750    
751
752    
753
754    
755      <!-- Hidden abstract block -->
756      <div class="abstract hidden">
757        <p>Learning by imitation provides an effective way for robots to develop well-regulated complex behaviors and directly benefit from natural demonstrations. State-of-the-art imitation learning (IL) approaches typically leverage Adversarial Motion Priors (AMP), which, despite their impressive results, suffer from two key limitations. They are prone to mode collapse, which often leads to overfitting to the simulation environment and thus increased sim-to-real gap, and they struggle to learn diverse behaviors effectively. To overcome these limitations, we introduce APEX (Action Priors enable Efficient eXploration): a simple yet versatile imitation learning framework that integrates demonstrations directly into reinforcement learning (RL), maintaining high exploration while grounding behavior with expert-informed priors. We achieve this through a combination of decaying action priors, which initially bias exploration toward expert demonstrations but gradually allow the policy to explore independently. This is complemented by a multi-critic RL framework that effectively balances stylistic consistency with task performance. Our approach achieves sample-efficient imitation learning and enables the acquisition of diverse skills within a single policy. APEX generalizes to varying velocities and preserves reference-like styles across complex tasks such as navigating rough terrain and climbing stairs, utilizing only flat-terrain kinematic motion data as a prior. We val
757idate our framework through extensive hardware experiments on the Unitree Go2 quadruped. There, APEX yields diverse and agile locomotion gaits, inherent gait transitions, and the highest reported speed for the platform to the best of our knowledge (peak velocity of  3.3 m/s on hardware). Our results establish APEX as a compelling alternative to existing IL methods, offering better efficiency, adaptability, and real-world performance.</p>
758      </div>
759    
760
761    
762      <!-- Hidden bibtex block -->
763      <div class="bibtex hidden">
764        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">sood2025apex</span><span class="p">,</span>
765  <span class="na">title</span> <span class="p">=</span> <span class="s">{APEX: Action Priors Enable Efficient Exploration for Skill Imitation on Articulated Robots}</span><span class="p">,</span>
766  <span class="na">author</span> <span class="p">=</span> <span class="s">{Sood, Shivam and Nakhwa, Laukik and Sun, Ge and Cao, Yuhong and Cheng, Jin and Zargarbashi, Fatemah and Yoon, Taerim and Choi, Sungjoon and Coros, Stelian and Sartoretti, Guillaume}</span><span class="p">,</span>
767  <span class="na">journal</span> <span class="p">=</span> <span class="s">{2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}</span><span class="p">,</span>
768  <span class="na">year</span> <span class="p">=</span> <span class="s">{2026}</span>
769<span class="p">}</span></code></pre></figure>
770      </div>
771    
772
773    
774  </div>
775</div>
776</li>
777</ol>
778<h2 class="bibliography">2025</h2>
779<ol class="bibliography">
780<li>
781<div class="row">
782  
783    <div class="col col-sm-2 abbr">
784      
785        
786          
787          
788
789<figure>
790  <picture>
791    <!-- Auto scaling with imagemagick -->
792    <!--
793      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
794      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
795    -->
796    
797      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/header-480.webp 480w,/assets/img/publication_preview/header-800.webp 800w,/assets/img/publication_preview/header-1400.webp 1400w," sizes="200px" type="image/webp"></source>
798    
799    <img src="/assets/img/publication_preview/header.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="header.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
800  </picture>
801
802  
803</figure>
804
805        
806      
807    </div>
808  
809
810  <!-- Entry bib key -->
811  <div id="cao2025header" class="col-sm-8">
812    <!-- Title -->
813    <div class="title">HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward</div>
814    <!-- Author -->
815    <div class="author">
816      
817
818      
819      <em>Yuhong
820            Cao</em>, Yizhuo
821            Wang, Jingsong
822            Liang, Shuhao
823            Liao, Yifeng
824            Zhang, Peizhuo
825            Li, and Guillaume
826            Sartoretti
827    </div>
828
829    <!-- Journal/Book title and date -->
830    
831    
832    
833    
834    
835    
836    
837    
838    
839    
840    <div class="periodical">
841      <em>Under review</em>,  2025
842    </div>
843    <div class="periodical">
844      
845    </div>
846
847    <!-- Links/Buttons -->
848    <div class="links">
849      
850      
851        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
852      
853      
854      
855        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
856      
857      
858      
859        
860          <a href="https://arxiv.org/pdf/2510.15679" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
861        
862      
863      
864      
865        <a href="https://drive.google.com/file/d/1aG3a5452QwMNHUftXHqKqVALj2yP-7KC/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
866      
867      
868      
869        <a href="https://github.com/marmotlab/ARiADNE2-ROS-Planner" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
870      
871      
872      
873      
874    </div>
875    
876      
877      
878      
879      
880    
881
882    
883
884    
885      <!-- Hidden abstract block -->
886      <div class="abstract hidden">
887        <p>This work pushes the boundaries of learning-based methods in autonomous robot exploration in terms of environmental scale and exploration efficie
887ncy. We present HEADER, an attention-based reinforcement learning approach with hierarchical graphs for efficient exploration in large-scale environments. HEADER follows existing conventional methods to construct hierarchical representations for the robot belief/map, but further designs a novel community-based algorithm to construct and update a global graph, which remains fully incremental, shape-adaptive, and operates with linear complexity. Building upon attention-based networks, our planner finely reasons about the nearby belief within the local range while coarsely leveraging distant information at the global scale, enabling next-best-viewpoint decisions that consider multi-scale spatial dependencies. Beyond novel map representation, we introduce a parameter-free privileged reward that significantly improves model performance and produces near-optimal exploration behaviors, by avoiding training objective bias caused by handcrafted reward shaping. In simulated challenging, large-scale exploration scenarios, HEADER demonstrates better scalability than most existing learning and non-learning methods, while achieving a significant improvement in exploration efficiency (up to 20%) over state-of-the-art baselines. We also deploy HEADER on hardware and validate it in complex, large-scale real-life scenarios, including a 300m*230m campus environment.</p>
888      </div>
889    
890
891    
892      <!-- Hidden bibtex block -->
893      <div class="bibtex hidden">
894        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">cao2025header</span><span class="p">,</span>
895  <span class="na">title</span> <span class="p">=</span> <span class="s">{HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward}</span><span class="p">,</span>
896  <span class="na">author</span> <span class="p">=</span> <span class="s">{Cao, Yuhong and Wang, Yizhuo and Liang, Jingsong and Liao, Shuhao and Zhang, Yifeng and Li, Peizhuo and Sartoretti, Guillaume}</span><span class="p">,</span>
897  <span class="na">journal</span> <span class="p">=</span> <span class="s">{Under review}</span><span class="p">,</span>
898  <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span>
899<span class="p">}</span></code></pre></figure>
900      </div>
901    
902
903    
904  </div>
905</div>
906</li>
907<li>
908<div class="row">
909  
910    <div class="col col-sm-2 abbr">
911      
912        
913          
914          
915
916<figure>
917  <picture>
918    <!-- Auto scaling with imagemagick -->
919    <!--
920      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
921      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
922    -->
923    
924      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/helom-480.webp 480w,/assets/img/publication_preview/helom-800.webp 800w,/assets/img/publication_preview/helom-1400.webp 1400w," sizes="200px" type="image/webp"></source>
925    
926    <img src="/assets/img/publication_preview/helom.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="helom.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
927  </picture>
928
929  
930</figure>
931
932        
933      
934    </div>
935  
936
937  <!-- Entry bib key -->
938  <div id="yang2025helom" class="col-sm-8">
939    <!-- Title -->
940    <div class="title">HeLoM: Hierarchical Learning for Whole-Body Loco-Manipulation in Hexapod Robot</div>
941    <!-- Author -->
942    <div class="author">
943      
944
945      
946      Xinrong
947            Yang, Peizhuo
948            Li, Hongyi
949            Li, Junkai
950            Lu, Linnan
951            Chang, <em>Yuhong
952            Cao</em>, Yifeng
953            Zhang, and
954        <span class="more-authors" title="click to view 2 more authors" onclick="
955              var element = $(this);
956              element.attr('title', '');
957              var more_authors_text = element.text() == '2 more authors' ? 'Ge Sun, Guillaume Sartoretti' : '2 more authors';
958              var cursorPosition = 0;
959              var textAdder = setInterval(function(){
960                element.text(more_authors_text.substring(0, cursorPosition + 1));
961                if (++cursorPosition == more_authors_text.length){
962                  clearInterval(textAdder);
963                }
964            }, '10');
965          ">2 more authors</span>
966      
967    </div>
968
969    <!-- Journal/Book title and date -->
970    
971    
972    
973    
974    
975    
976    
977    
978    
979    
980    <div class="periodical">
981      <em>Under review</em>,  2025
982    </div>
983    <div class="periodical">
984      
985    </div>
986
987    <!-- Links/Buttons -->
988    <div class="links">
989      
990      
991        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
992      
993      
994      
995        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
996      
997      
998      
999        
1000          <a href="https://arxiv.org/abs/2509.23651" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
1001        
1002      
1003      
1004      
1005        <a href="https://drive.google.com/file/d/1jDrqZTLF5Ub1jWsbQDRULE2UeKcGldxu/view?usp=drivesdk" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
1006      
1007      
1008      
1009      
1010      
1011      
1012    </div>
1013    
1014      
1015      
1016      
1017      
1018    
1019
1020    
1021
1022    
1023      <!-- Hidden abstract block -->
1024      <div class="abstract hidden">
1025        <p>Robots in real-world environments are often required to move/manipulate objects comparable in weight to their own bodies. Compared to grasping and carrying, pushing provides a more straightforward and efficie
1025nt non-prehensile manipulation strategy, avoiding complex grasp design while leveraging direct contact to regulate an object’s pose. Achieving effective pushing, however, demands both sufficient manipulation forces and the ability to maintain stability, which is particularly challenging when dealing with heavy or irregular objects. To address these challenges, we propose HeLoM, a learning-based hierarchical whole-body manipulation framework for a hexapod robot that exploits coordinated multi-limb control. Inspired by the cooperative strategies of multi-legged insects, our framework leverages redundant contact points and high degrees of freedom to enable dynamic redistribution of contact forces. HeLoM’s high-level planner plans pushing behaviors and target object poses, while its low-level controller maintains locomotion stability and generates dynamically consistent joint actions. Our policies trained in simulation are directly deployed on real robots without additional fine-tuning. This design allows the robot to maintain balance while exerting continuous and controllable pushing forces through coordinated foreleg interaction and supportive hind-leg propulsion. We validate the effectiveness of HeLoM through both simulation and real-world experiments. Results show that our framework can stably push boxes of varying sizes and unknown physical properties to designated goal poses in the real world.</p>
1026      </div>
1027    
1028
1029    
1030      <!-- Hidden bibtex block -->
1031      <div class="bibtex hidden">
1032        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">yang2025helom</span><span class="p">,</span>
1033  <span class="na">journal</span> <span class="p">=</span> <span class="s">{Under review}</span><span class="p">,</span>
1034  <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span>
1035  <span class="na">title</span> <span class="p">=</span> <span class="s">{HeLoM: Hierarchical Learning for Whole-Body Loco-Manipulation in Hexapod Robot}</span><span class="p">,</span>
1036  <span class="na">author</span> <span class="p">=</span> <span class="s">{Yang, Xinrong and Li, Peizhuo and Li, Hongyi and Lu, Junkai and Chang, Linnan and Cao, Yuhong and Zhang, Yifeng and Sun, Ge and Sartoretti, Guillaume}</span>
1037<span class="p">}</span></code></pre></figure>
1038      </div>
1039    
1040
1041    
1042  </div>
1043</div>
1044</li>
1045<li>
1046<div class="row">
1047  
1048    <div class="col col-sm-2 abbr">
1049      
1050        
1051          
1052          
1053
1054<figure>
1055  <picture>
1056    <!-- Auto scaling with imagemagick -->
1057    <!--
1058      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
1059      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
1060    -->
1061    
1062      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/cogniplan.webp-480.webp 480w,/assets/img/publication_preview/cogniplan.webp-800.webp 800w,/assets/img/publication_preview/cogniplan.webp-1400.webp 1400w," sizes="200px" type="image/webp"></source>
1063    
1064    <img src="/assets/img/publication_preview/cogniplan.webp" class="preview z-depth-1 rounded" width="100%" height="auto" alt="cogniplan.webp" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
1065  </picture>
1066
1067  
1068</figure>
1069
1070        
1071      
1072    </div>
1073  
1074
1075  <!-- Entry bib key -->
1076  <div id="wang2025cogniplan" class="col-sm-8">
1077    <!-- Title -->
1078    <div class="title">CogniPlan: Uncertainty-Guided Path Planning with Conditional Generative Layout Prediction</div>
1079    <!-- Author -->
1080    <div class="author">
1081      
1082
1083      
1084      Yizhuo
1085            Wang, Haodong
1086            He, Jingsong
1087            Liang, <em>Yuhong
1088            Cao</em>, Ritabrata
1089            Chakraborty, and Guillaume
1090            Sartoretti
1091    </div>
1092
1093    <!-- Journal/Book title and date -->
1094    
1095    
1096    
1097    
1098    
1099    
1100    
1101    
1102    
1103    
1104    <div class="periodical">
1105      <em>Conference on Robot Learning</em>,  2025
1106    </div>
1107    <div class="periodical">
1108      
1109    </div>
1110
1111    <!-- Links/Buttons -->
1112    <div class="links">
1113      
1114      
1115        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
1116      
1117      
1118      
1119        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1120      
1121      
1122      
1123        
1124          <a href="https://www.arxiv.org/pdf/2508.03027" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
1125        
1126      
1127      
1128      
1129        <a href="https://drive.google.com/file/d/1j-d2frHjUxDTm14VGeuwWLqowxG82SpT/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
1130      
1131      
1132      
1133      
1134      
1135      
1136        <a href="https://yizhuo-wang.com/cogniplan/" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Website</a>
1137      
1138    </div>
1139    
1140      
1141      
1142      
1143      
1144    
1145
1146    
1147
1148    
1149      <!-- Hidden abstract block -->
1150      <div class="abstract hidden">
1151        <p>Path planning in unknown environments is a crucial yet inherently challenging capability for mobile robots, which primarily encompasses two coupled tasks: autonomous exploration and point-goal navigation. In both cases, the robot must perceive the environment, update its belief, and accurately estimate potential information gain on-the-fly to guide planning. In this work, we propose CogniPlan, a novel path planning framework that leverages multiple plausible layouts predicted by a COnditional GeNerative Inpainting model, mirroring how humans rely on cognitive maps during navigation. These predictions, based on the partially observed map and a set of layout conditioning vectors, enable our planner to reason effectively under uncertainty. We demonstrate strong synergy between generative image-based layout prediction and graph-attention-based path planning, allowing CogniPlan to combine the scalability of graph representations with the fidelity and predictiveness of occupancy maps, yielding notable performance gains in both exploration and navigation. We extensively evaluate CogniPlan on two datasets (hundreds of maps and realistic floor plans), consistently outperforming state-of-the-art planners. We further deploy it in a high-fidelity simulator and on hardware, showcasing its high-quality path planning and real-world applicability.</p>
1152      </div>
1153    
1154
1155    
1156      <!-- Hidden bibtex block -->
1157      <div class="bibtex hidden">
1158        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">wang2025cogniplan</span><span class="p">,</span>
1159  <span class="na">title</span> <span class="p">=</span> <span class="s">{CogniPlan: Uncertainty-Guided Path Planning with Conditional Generative Layout Prediction}</span><span class="p">,</span>
1160  <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Yizhuo and He, Haodong and Liang, Jingsong and Cao, Yuhong and Chakraborty, Ritabrata and Sartoretti, Guillaume}</span><span class="p">,</span>
1161  <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span>
1162  <span class="na">journal</span> <span class="p">=</span> <span class="s">{Conference on Robot Learning}</span>
1163<span class="p">}</span></code></pre></figure>
1164      </div>
1165    
1166
1167    
1168  </div>
1169</div>
1170</li>
1171<li>
1172<div class="row">
1173  
1174    <div class="col col-sm-2 abbr">
1175      
1176        
1177          
1178          
1179
1180<figure>
1181  <picture>
1182    <!-- Auto scaling with imagemagick -->
1183    <!--
1184      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
1185      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
1186    -->
1187    
1188      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/search_tta-480.webp 480w,/assets/img/publication_preview/search_tta-800.webp 800w,/assets/img/publication_preview/search_tta-1400.webp 1400w," sizes="200px" type="image/webp"></source>
1189    
1190    <img src="/assets/img/publication_preview/search_tta.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="search_tta.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
1191  </picture>
1192
1193  
1194</figure>
1195
1196        
1197      
1198    </div>
1199  
1200
1201  <!-- Entry bib key -->
1202  <div id="tan2025searchtta" class="col-sm-8">
1203    <!-- Title -->
1204    <div class="title">Search-TTA: A Multi-Modal Test-Time Adaptation Framework for Visual Search in the Wild</div>
1205    <!-- Author -->
1206    <div class="author">
1207      
1208
1209      
1210      Derek Ming Siang
1211            Tan, Boyang
1212            Liu, Alok
1213            Raj, Qi Xuan
1214            Ang, Weiheng
1215            Dai, Tanishq
1216            Duhan, Jimmy
1217            Chiun, and
1218        <span class="more-authors" title="click to view 3 more authors" onclick="
1219              var element = $(this);
1220              element.attr('title', '');
1221              var more_authors_text = element.text() == '3 more authors' ? 'Yuhong Cao, Florian Shkurti, Guillaume Sartoretti' : '3 more authors';
1222              var cursorPosition = 0;
1223              var textAdder = setInterval(function(){
1224                element.text(more_authors_text.substring(0, cursorPosition + 1));
1225                if (++cursorPosition == more_authors_text.length){
1226                  clearInterval(textAdder);
1227                }
1228            }, '10');
1229          ">3 more authors</span>
1230      
1231    </div>
1232
1233    <!-- Journal/Book title and date -->
1234    
1235    
1236    
1237    
1238    
1239    
1240    
1241    
1242    
1243    
1244    <div class="periodical">
1245      <em>Conference on Robot Learning</em>,  2025
1246    </div>
1247    <div class="periodical">
1248      
1249    </div>
1250
1251    <!-- Links/Buttons -->
1252    <div class="links">
1253      
1254      
1255        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
1256      
1257      
1258      
1259        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1260      
1261      
1262      
1263        
1264          <a href="https://arxiv.org/pdf/2505.11350" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
1265        
1266      
1267      
1268      
1269        <a href="https://drive.google.com/file/d/1X4U7iSIDV07qIgsWtn4i6fNr-KKJ2_8X/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
1270      
1271      
1272      
1273      
1274      
1275      
1276        <a href="https://search-tta.github.io/" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Website</a>
1277      
1278    </div>
1279    
1280      
1281      
1282      
1283      
1284    
1285
1286    
1287
1288    
1289      <!-- Hidden abstract block -->
1290      <div class="abstract hidden">
1291        <p>To perform autonomous visual search for environmental monitoring, a robot may leverage satellite imagery as a prior map. This can help inform coarse, high level search and exploration strategies, even when such images lack sufficient resolution to allow fine-grained, explicit visual recognition of targets. However, there are some challenges to overcome with using satellite images to direct visual search. For one, targets that are unseen in satellite images are underrepresented (compared to real life) in most existing datasets, and thus vision models trained on these datasets fail to reason effectively based on indirect visual cues. Furthermore, approaches which leverage large Vision Language Models (VLMs) for generalization may yield inaccurate outputs due to hallucination, leading to inefficient search. To address these challenges, we introduce Search-TTA, a multimodal test-time adaptation framework that can accept text and/or image input. First, we pretrain a remote sensing image encoder to align with CLIP’s visual encoder to output probability distributions of target presence used for visual search. Second, our framework dynamically refines CLIP’s predictions during search using a test-time adaptation mechanism. Through a feedback loop inspired by Spatial Poisson Point Processes, gradient updates (weighted by uncertainty) are used to correct (potentially inaccurate) predictions and improve search performance. To validate Search-TTA’s performance, we curate a visual search dataset based on internet-scale ecological data. We find that Search-TTA improves planner performance by up to 9.7%, particularly in cases with poor initial CLIP predictions. It also achieves comparable performance to state-of-the-art VLMs. Finally, we deploy Search-TTA on a real UAV via hardware-in-the-loop testing, by simulating its operation within a large-scale simulation that provides onboard sensing.</p>
1292      </div>
1293    
1294
1295    
1296      <!-- Hidden bibtex block -->
1297      <div class="bibtex hidden">
1298        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">tan2025searchtta</span><span class="p">,</span>
1299  <span class="na">title</span> <span class="p">=</span> <span class="s">{Search-TTA: A Multi-Modal Test-Time Adaptation Framework for Visual Search in the Wild}</span><span class="p">,</span>
1300  <span class="na">author</span> <span class="p">=</span> <span class="s">{Tan, Derek Ming Siang and Liu, Boyang and Raj, Alok and Ang, Qi Xuan and Dai, Weiheng and Duhan, Tanishq and Chiun, Jimmy and Cao, Yuhong and Shkurti, Florian and Sartoretti, Guillaume}</span><span class="p">,</span>
1301  <span class="na">
1301journal</span> <span class="p">=</span> <span class="s">{Conference on Robot Learning}</span><span class="p">,</span>
1302  <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span>
1303<span class="p">}</span></code></pre></figure>
1304      </div>
1305    
1306
1307    
1308  </div>
1309</div>
1310</li>
1311<li>
1312<div class="row">
1313  
1314    <div class="col col-sm-2 abbr">
1315      
1316        
1317          
1318          
1319
1320<figure>
1321  <picture>
1322    <!-- Auto scaling with imagemagick -->
1323    <!--
1324      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
1325      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
1326    -->
1327    
1328      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/sata-480.webp 480w,/assets/img/publication_preview/sata-800.webp 800w,/assets/img/publication_preview/sata-1400.webp 1400w," sizes="200px" type="image/webp"></source>
1329    
1330    <img src="/assets/img/publication_preview/sata.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="sata.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
1331  </picture>
1332
1333  
1334</figure>
1335
1336        
1337      
1338    </div>
1339  
1340
1341  <!-- Entry bib key -->
1342  <div id="li2025sata" class="col-sm-8">
1343    <!-- Title -->
1344    <div class="title">SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by Animal Learning</div>
1345    <!-- Author -->
1346    <div class="author">
1347      
1348
1349      
1350      Peizhuo
1351            Li, Hongyi
1352            Li, Ge
1353            Sun, Jin
1354            Cheng, Xinrong
1355            Yang, Guillaume
1356            Bellegarda, Milad
1357            Shafiee, and
1358        <span class="more-authors" title="click to view 3 more authors" onclick="
1359              var element = $(this);
1360              element.attr('title', '');
1361              var more_authors_text = element.text() == '3 more authors' ? 'Yuhong Cao, Auke Ijspeert, Guillaume Sartoretti' : '3 more authors';
1362              var cursorPosition = 0;
1363              var textAdder = setInterval(function(){
1364                element.text(more_authors_text.substring(0, cursorPosition + 1));
1365                if (++cursorPosition == more_authors_text.length){
1366                  clearInterval(textAdder);
1367                }
1368            }, '10');
1369          ">3 more authors</span>
1370      
1371    </div>
1372
1373    <!-- Journal/Book title and date -->
1374    
1375    
1376    
1377    
1378    
1379    
1380    
1381    
1382    
1383    
1384    <div class="periodical">
1385      <em>2025 Robotics: Science and Systems (RSS)</em>,  2025
1386    </div>
1387    <div class="periodical">
1388      
1389    </div>
1390
1391    <!-- Links/Buttons -->
1392    <div class="links">
1393      
1394      
1395        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
1396      
1397      
1398      
1399        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1400      
1401      
1402      
1403        
1404          <a href="https://www.arxiv.org/pdf/2502.12674" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
1405        
1406      
1407      
1408      
1409        <a href="https://drive.google.com/file/d/1PLxFbyujH_4cdFEy0wmO5gsHDFZASIUV/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
1410      
1411      
1412      
1413        <a href="https://github.com/marmotlab/SATA" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
1414      
1415      
1416      
1417      
1418    </div>
1419    
1420      
1421      
1422      
1423      
1424    
1425
1426    
1427
1428    
1429      <!-- Hidden abstract block -->
1430      <div class="abstract hidden">
1431        <p>Despite recent advances in learning-based controllers for legged robots, deployments in human-centric environments remain limited by safety concerns. Most of these approaches use position-based control, where policies output target joint angles that must be processed by a low-level controller (e.g., PD or impedance controllers) to compute joint torques. Although impressive results have been achieved in controlled real-world scenarios, these methods often struggle with compliance and adaptability when encountering environments or disturbances unseen during training, potentially resulting in extreme or unsafe behaviors. Inspired by how animals achieve smooth and adaptive movements by controlling muscle extension and contraction, torque-based policies offer a promising alternative by enabling precise and direct control of the actuators in torque space. In principle, this approach facilitates more effective interactions with the environment, resulting in safer and more adaptable behaviors. However, challenges such as a highly nonlinear state space and inefficient exploration during training have hindered their broader adoption. To address these limitations, we propose SATA, a bio-inspired framework that mimics key biomechanical principles and adaptive learning mechanisms observed in animal locomotion. Our approach effectively addresses the inherent challenges of learning torque-based policies by significantly improving early-stage exploration, leading to high-performance final policies. Remarkably, our method achieves zero-shot sim-to-real transfer. Our experimental results indicate that SATA demonstrates remarkable compliance and safety, even in challenging environments such as soft/slippery terrain or narrow passages, and under significant external disturbances, highlighting its potential for practical deployments in human-centric and safety-critical scenarios.</p>
1432      </div>
1433    
1434
1435    
1436      <!-- Hidden bibtex block -->
1437      <div class="bibtex hidden">
1438        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">li2025sata</span><span class="p">,</span>
1439  <span class="na">title</span> <span class="p">=</span> <span class="s">
1439{SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by Animal Learning}</span><span class="p">,</span>
1440  <span class="na">author</span> <span class="p">=</span> <span class="s">{Li, Peizhuo and Li, Hongyi and Sun, Ge and Cheng, Jin and Yang, Xinrong and Bellegarda, Guillaume and Shafiee, Milad and Cao, Yuhong and Ijspeert, Auke and Sartoretti, Guillaume}</span><span class="p">,</span>
1441  <span class="na">journal</span> <span class="p">=</span> <span class="s">{2025 Robotics: Science and Systems (RSS)}</span><span class="p">,</span>
1442  <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span>
1443<span class="p">}</span></code></pre></figure>
1444      </div>
1445    
1446
1447    
1448  </div>
1449</div>
1450</li>
1451<li>
1452<div class="row">
1453  
1454    <div class="col col-sm-2 abbr">
1455      
1456        
1457          
1458          
1459
1460<figure>
1461  <picture>
1462    <!-- Auto scaling with imagemagick -->
1463    <!--
1464      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
1465      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
1466    -->
1467    
1468      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/marvel-480.webp 480w,/assets/img/publication_preview/marvel-800.webp 800w,/assets/img/publication_preview/marvel-1400.webp 1400w," sizes="200px" type="image/webp"></source>
1469    
1470    <img src="/assets/img/publication_preview/marvel.png" class="preview z-depth-1 rounded" width="100%" height="auto" alt="marvel.png" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
1471  </picture>
1472
1473  
1474</figure>
1475
1476        
1477      
1478    </div>
1479  
1480
1481  <!-- Entry bib key -->
1482  <div id="chiun2025marvel" class="col-sm-8">
1483    <!-- Title -->
1484    <div class="title">MARVEL: Multi-Agent Reinforcement Learning for constrained field-of-View multi-robot Exploration in Large-scale environments</div>
1485    <!-- Author -->
1486    <div class="author">
1487      
1488
1489      
1490      Jimmy
1491            Chiun, Shizhe
1492            Zhang, Yizhuo
1493            Wang, <em>Yuhong
1494            Cao</em>, and Guillaume
1495            Sartoretti
1496    </div>
1497
1498    <!-- Journal/Book title and date -->
1499    
1500    
1501    
1502    
1503    
1504    
1505    
1506    
1507    
1508    
1509    <div class="periodical">
1510      <em>2025 IEEE International Conference on Robotics and Automation (ICRA)</em>,  2025
1511    </div>
1512    <div class="periodical">
1513      
1514    </div>
1515
1516    <!-- Links/Buttons -->
1517    <div class="links">
1518      
1519      
1520        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
1521      
1522      
1523      
1524        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1525      
1526      
1527      
1528        
1529          <a href="https://marmotlab.org/publications/77-ICRA2025-MARVEL.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
1530        
1531      
1532      
1533      
1534        <a href="https://www.youtube.com/watch?v=uwQqkruBvwY" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
1535      
1536      
1537      
1538        <a href="https://github.com/marmotlab/MARVEL" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
1539      
1540      
1541      
1542      
1543    </div>
1544    
1545      
1546      
1547      
1548      
1549    
1550
1551    
1552
1553    
1554      <!-- Hidden abstract block -->
1555      <div class="abstract hidden">
1556        <p>In multi-robot exploration, a team of mobile robot is tasked with efficiently mapping an unknown environments. While most exploration planners assume omnidirectional sensors like LiDAR, this is impractical for small robots such as drones, where lightweight, directional sensors like cameras may be the only option due to payload constraints. These sensors have a constrained field-of-view (FoV), which adds complexity to the exploration problem, requiring not only optimal robot positioning but also sensor orientation during movement. In this work, we propose MARVEL, a neural framework that leverages graph attention networks, together with novel frontiers and orientation features fusion technique, to develop a collaborative, decentralized policy using multi-agent reinfor
1556cement learning (MARL) for robots with constrained FoV. To handle the large action space of viewpoints planning, we further introduce a novel information-driven action pruning strategy. MARVEL improves multi-robot coordination and decision-making in challenging large-scale indoor environments, while adapting to various team sizes and sensor configurations (ie, FoV and sensor range) without additional training. Our extensive evaluation shows that MARVEL’s learned policies exhibit effective coordinated behaviors, outperforming state-ofthe-art exploration planners across multiple metrics. We experimentally demonstrate MARVEL’s generalizability in large-scale environments, of up to 90m by 90m, and validate its practical applicability through successful deployment on a team of real drone hardware.</p>
1557      </div>
1558    
1559
1560    
1561      <!-- Hidden bibtex block -->
1562      <div class="bibtex hidden">
1563        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">chiun2025marvel</span><span class="p">,</span>
1564  <span class="na">title</span> <span class="p">=</span> <span class="s">{MARVEL: Multi-Agent Reinforcement Learning for constrained field-of-View multi-robot Exploration in Large-scale environments}</span><span class="p">,</span>
1565  <span class="na">author</span> <span class="p">=</span> <span class="s">{Chiun, Jimmy and Zhang, Shizhe and Wang, Yizhuo and Cao, Yuhong and Sartoretti, Guillaume}</span><span class="p">,</span>
1566  <span class="na">journal</span> <span class="p">=</span> <span class="s">{2025 IEEE International Conference on Robotics and Automation (ICRA)}</span><span class="p">,</span>
1567  <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span>
1568  <span class="na">publisher</span> <span class="p">=</span> <span class="s">{IEEE}</span>
1569<span class="p">}</span></code></pre></figure>
1570      </div>
1571    
1572
1573    
1574  </div>
1575</div>
1576</li>
1577<li>
1578<div class="row">
1579  
1580    <div class="col col-sm-2 abbr">
1581      
1582        
1583          
1584          
1585
1586<figure>
1587  <picture>
1588    <!-- Auto scaling with imagemagick -->
1589    <!--
1590      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
1591      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
1592    -->
1593    
1594      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/sigma-480.webp 480w,/assets/img/publication_preview/sigma-800.webp 800w,/assets/img/publication_preview/sigma-1400.webp 1400w," sizes="200px" type="image/webp"></source>
1595    
1596    <img src="/assets/img/publication_preview/sigma.png" class="preview z-depth-1 rounded" width="100%" height="auto" alt="sigma.png" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
1597  </picture>
1598
1599  
1600</figure>
1601
1602        
1603      
1604    </div>
1605  
1606
1607  <!-- Entry bib key -->
1608  <div id="liao2025sigma" class="col-sm-8">
1609    <!-- Title -->
1610    <div class="title">SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding</div>
1611    <!-- Author -->
1612    <div class="author">
1613      
1614
1615      
1616      Shuhao
1617            Liao, Weihang
1618            Xia, <em>Yuhong
1619            Cao</em>, Weiheng
1620            Dai, Chengyang
1621            He, Wenjun
1622            Wu, and Guillaume
1623            Sartoretti
1624    </div>
1625
1626    <!-- Journal/Book title and date -->
1627    
1628    
1629    
1630    
1631    
1632    
1633    
1634    
1635    
1636    
1637    <div class="periodical">
1638      <em>2025 IEEE International Conference on Robotics and Automation (ICRA)</em>,  2025
1639    </div>
1640    <div class="periodical">
1641      
1642    </div>
1643
1644    <!-- Links/Buttons -->
1645    <div class="links">
1646      
1647      
1648        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
1649      
1650      
1651      
1652        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1653      
1654      
1655      
1656        
1657          <a href="https://arxiv.org/pdf/2502.06440" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
1658        
1659      
1660      
1661      
1662      
1663      
1664        <a href="https://github.com/marmotlab/SIGMA" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
1665      
1666      
1667      
1668      
1669    </div>
1670    
1671      
1672      
1673      
1674      
1675    
1676
1677    
1678
1679    
1680      <!-- Hidden abstract block -->
1681      <div class="abstract hidden">
1682        <p>The Multi-Agent Path Finding (MAPF) problem aims to determine the shortest and collision-free paths for multiple agents in a known, potentially obstacle-ridden environment. It is the core challenge for robotic deployments in large-scale logistics and transportation. Decentralized learning-based approaches have shown great potential for addressing the MAPF problems, offering more reactive and scalable solutions. However, existing learning-based MAPF methods usually rely on agents making decisions based on a limited field of view (FOV), resulting in short-sighted policies and inefficient cooperation in complex scenarios. There, a critical challenge is to achieve consensus on potential movements between agents based on limited observations and communications. To tackle this challenge, we introduce a new framework that applies sheaf theory to decentralized deep reinforcement learning, enabling agents to learn geometric cross-dependencies between each other through local consensus and utilize them for tightly cooperative decision-making. In particular, sheaf theory provides a mathematical proof of conditions for achieving global consensus through local observation. Inspired by this, we incorporate a neural network to approximately model the consensus in latent space based on sheaf theory and train it through self-supervised learning. During the task, in addition to normal features for MAPF as in previous works, each agent distributedly reasons about a learned consensus feature, leading to efficie
1682nt cooperation on pathfinding and collision avoidance. As a result, our proposed method demonstrates significant improvements over state-of-the-art learning-based MAPF planners, especially in relatively large and complex scenarios, demonstrating its superiority over baselines in various simulations and real-world robot experiments.</p>
1683      </div>
1684    
1685
1686    
1687      <!-- Hidden bibtex block -->
1688      <div class="bibtex hidden">
1689        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">liao2025sigma</span><span class="p">,</span>
1690  <span class="na">title</span> <span class="p">=</span> <span class="s">{SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding}</span><span class="p">,</span>
1691  <span class="na">author</span> <span class="p">=</span> <span class="s">{Liao, Shuhao and Xia, Weihang and Cao, Yuhong and Dai, Weiheng and He, Chengyang and Wu, Wenjun and Sartoretti, Guillaume}</span><span class="p">,</span>
1692  <span class="na">journal</span> <span class="p">=</span> <span class="s">{2025 IEEE International Conference on Robotics and Automation (ICRA)}</span><span class="p">,</span>
1693  <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span>
1694  <span class="na">publisher</span> <span class="p">=</span> <span class="s">{IEEE}</span>
1695<span class="p">}</span></code></pre></figure>
1696      </div>
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1716      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
1717      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
1718    -->
1719    
1720      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/dare-480.webp 480w,/assets/img/publication_preview/dare-800.webp 800w,/assets/img/publication_preview/dare-1400.webp 1400w," sizes="200px" type="image/webp"></source>
1721    
1722    <img src="/assets/img/publication_preview/dare.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="dare.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
1723  </picture>
1724
1725  
1726</figure>
1727
1728        
1729      
1730    </div>
1731  
1732
1733  <!-- Entry bib key -->
1734  <div id="cao2025dare" class="col-sm-8">
1735    <!-- Title -->
1736    <div class="title">DARE: Diffusion Policy for Autonomous Robot Exploration</div>
1737    <!-- Author -->
1738    <div class="author">
1739      
1740
1741      
1742      <em>Yuhong
1743            Cao</em>, Jeric
1744            Lew, Jingsong
1745            Liang, Jin
1746            Cheng, and Guillaume
1747            Sartoretti
1748    </div>
1749
1750    <!-- Journal/Book title and date -->
1751    
1752    
1753    
1754    
1755    
1756    
1757    
1758    
1759    
1760    
1761    <div class="periodical">
1762      <em>2025 IEEE International Conference on Robotics and Automation (ICRA)</em>,  2025
1763    </div>
1764    <div class="periodical">
1765      
1766    </div>
1767
1768    <!-- Links/Buttons -->
1769    <div class="links">
1770      
1771      
1772        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
1773      
1774      
1775      
1776        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1777      
1778      
1779      
1780        
1781          <a href="https://arxiv.org/pdf/2410.16687" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
1782        
1783      
1784      
1785      
1786        <a href="https://drive.google.com/file/d/1fMzJoOQKK_nfPjQDta6mOIjewsQhN4rB/view?usp=drive_link" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
1787      
1788      
1789      
1790        <a href="https://github.com/marmotlab/DARE" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
1791      
1792      
1793      
1794      
1795    </div>
1796    
1797      
1798      
1799      
1800      
1801    
1802
1803    
1804
1805    
1806      <!-- Hidden abstract block -->
1807      <div class="abstract hidden">
1808        <p>
1808Autonomous robot exploration requires a robot to efficiently explore and map unknown environments. Compared to conventional methods that can only optimize paths based on the current robot belief, learning-based methods show the potential to achieve improved performance by drawing on past experiences to reason about unknown areas. In this paper, we propose DARE, a novel generative approach that leverages diffusion models trained on expert demonstrations, which can explicitly generate an exploration path through one-time inference. We build DARE upon an attention-based encoder and a diffusion policy model, and introduce ground truth optimal demonstrations for training to learn better patterns for exploration. The trained planner can reason about the partial belief to recognize the potential structure in unknown areas and consider these areas during path planning. Our experiments demonstrate that DARE achieves on-par performance with both conventional and learning-based state-of-the-art exploration planners, as well as good generalizability in both simulations and real-life scenarios.</p>
1809      </div>
1810    
1811
1812    
1813      <!-- Hidden bibtex block -->
1814      <div class="bibtex hidden">
1815        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">cao2025dare</span><span class="p">,</span>
1816  <span class="na">title</span> <span class="p">=</span> <span class="s">{DARE: Diffusion Policy for Autonomous Robot Exploration}</span><span class="p">,</span>
1817  <span class="na">author</span> <span class="p">=</span> <span class="s">{Cao, Yuhong and Lew, Jeric and Liang, Jingsong and Cheng, Jin and Sartoretti, Guillaume}</span><span class="p">,</span>
1818  <span class="na">journal</span> <span class="p">=</span> <span class="s">{2025 IEEE International Conference on Robotics and Automation (ICRA)}</span><span class="p">,</span>
1819  <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span>
1820  <span class="na">publisher</span> <span class="p">=</span> <span class="s">{IEEE}</span>
1821<span class="p">}</span></code></pre></figure>
1822      </div>
1823    
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1827</div>
1828</li>
1829<li>
1830<div class="row">
1831  
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1833      
1834        
1835          
1836          
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1838<figure>
1839  <picture>
1840    <!-- Auto scaling with imagemagick -->
1841    <!--
1842      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
1843      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
1844    -->
1845    
1846      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/hetmrta-480.webp 480w,/assets/img/publication_preview/hetmrta-800.webp 800w,/assets/img/publication_preview/hetmrta-1400.webp 1400w," sizes="200px" type="image/webp"></source>
1847    
1848    <img src="/assets/img/publication_preview/hetmrta.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="hetmrta.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
1849  </picture>
1850
1851  
1852</figure>
1853
1854        
1855      
1856    </div>
1857  
1858
1859  <!-- Entry bib key -->
1860  <div id="dai2025heterogeneous" class="col-sm-8">
1861    <!-- Title -->
1862    <div class="title">Heterogeneous Multi-robot Task Allocation and Scheduling via Reinforcement Learning</div>
1863    <!-- Author -->
1864    <div class="author">
1865      
1866
1867      
1868      Weiheng
1869            Dai, Utkarsh
1870            Rai, Jimmy
1871            Chiun, <em>Yuhong
1872            Cao</em>, and Guillaume
1873            Sartoretti
1874    </div>
1875
1876    <!-- Journal/Book title and date -->
1877    
1878    
1879    
1880    
1881    
1882    
1883    
1884    
1885    
1886    
1887    <div class="periodical">
1888      <em>IEEE Robotics and Automation Letters</em>,  2025
1889    </div>
1890    <div class="periodical">
1891      
1892    </div>
1893
1894    <!-- Links/Buttons -->
1895    <div class="links">
1896      
1897      
1898        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
1899      
1900      
1901      
1902        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1903      
1904      
1905      
1906        
1907          <a href="https://marmotlab.org/publications/73-RAL2025-HetMRTA.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
1908        
1909      
1910      
1911      
1912        <a href="https://drive.google.com/file/d/1nJze4E4Unl7p0mQH0YpJVnVVVzxU5ruR/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
1913      
1914      
1915      
1916        <a href="https://github.com/marmotlab/HeteroMRTA" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
1917      
1918      
1919      
1920      
1921    </div>
1922    
1923      
1924      
1925      
1926      
1927    
1928
1929    
1930
1931    
1932      <!-- Hidden abstract block -->
1933      <div class="abstract hidden">
1934        <p>Many multi-robot applications require allocating a team of heterogeneous agents (robots) with different abilities to cooperatively complete a given set of spatially distributed tasks as quickly as possible. We focus on tasks that can only be initiated when all required agents are present otherwise arrived agents would be waiting idly. Agents need to not only execute a sequence of tasks by dynamically forming and disbanding teams to satisfy/match diverse ability requirements of each task but also account for the schedules of other agents to minimize unnecessary idle time. Conventional methods such as mix-integer programming generally require centralized scheduling and a long optimization time, which limits their potential for real-world applications. In this work, we propose a reinforcement learning framework to train a decentralized policy applicable to heterogeneous agents. To address the challenge of complex cooperation learning, we further introduce a constrained flashforward mechanism to guide/constrain the agents’ exploration and help them make better predictions. Through an attention mechanism that reasons about both 
1934short-term cooperation and long-term scheduling dependency, agents learn to reactively choose their next tasks (and subsequent coalitions) to avoid wasting abilities and to shorten the overall task completion time (makespan). We compare our method with state-of-the-art heuristic and mixed-integer programming methods, demonstrating its generalization ability and showing it closely matches or outperforms these baselines while remaining at least two orders of magnitude faster.</p>
1935      </div>
1936    
1937
1938    
1939      <!-- Hidden bibtex block -->
1940      <div class="bibtex hidden">
1941        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">dai2025heterogeneous</span><span class="p">,</span>
1942  <span class="na">title</span> <span class="p">=</span> <span class="s">{Heterogeneous Multi-robot Task Allocation and Scheduling via Reinforcement Learning}</span><span class="p">,</span>
1943  <span class="na">author</span> <span class="p">=</span> <span class="s">{Dai, Weiheng and Rai, Utkarsh and Chiun, Jimmy and Cao, Yuhong and Sartoretti, Guillaume}</span><span class="p">,</span>
1944  <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Robotics and Automation Letters}</span><span class="p">,</span>
1945  <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span>
1946  <span class="na">publisher</span> <span class="p">=</span> <span class="s">{IEEE}</span>
1947<span class="p">}</span></code></pre></figure>
1948      </div>
1949    
1950
1951    
1952  </div>
1953</div>
1954</li>
1955</ol>
1956<h2 class="bibliography">2024</h2>
1957<ol class="bibliography">
1958<li>
1959<div class="row">
1960  
1961    <div class="col col-sm-2 abbr">
1962      
1963        
1964          
1965          
1966
1967<figure>
1968  <picture>
1969    <!-- Auto scaling with imagemagick -->
1970    <!--
1971      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
1972      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
1973    -->
1974    
1975      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/hdplanner-480.webp 480w,/assets/img/publication_preview/hdplanner-800.webp 800w,/assets/img/publication_preview/hdplanner-1400.webp 1400w," sizes="200px" type="image/webp"></source>
1976    
1977    <img src="/assets/img/publication_preview/hdplanner.png" class="preview z-depth-1 rounded" width="100%" height="auto" alt="hdplanner.png" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
1978  </picture>
1979
1980  
1981</figure>
1982
1983        
1984      
1985    </div>
1986  
1987
1988  <!-- Entry bib key -->
1989  <div id="liang2024hdplanner" class="col-sm-8">
1990    <!-- Title -->
1991    <div class="title">HDPlanner: Advancing Autonomous Deployments in Unknown Environments through Hierarchical Decision Networks</div>
1992    <!-- Author -->
1993    <div class="author">
1994      
1995
1996      
1997      Jingsong
1998            Liang, <em>Yuhong
1999            Cao</em>, Yixiao
2000            Ma, Hanqi
2001            Zhao, and Guillaume
2002            Sartoretti
2003    </div>
2004
2005    <!-- Journal/Book title and date -->
2006    
2007    
2008    
2009    
2010    
2011    
2012    
2013    
2014    
2015    
2016    <div class="periodical">
2017      <em>IEEE Robotics and Automation Letters</em>,  2024
2018    </div>
2019    <div class="periodical">
2020      
2021    </div>
2022
2023    <!-- Links/Buttons -->
2024    <div class="links">
2025      
2026      
2027        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2028      
2029      
2030      
2031        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2032      
2033      
2034      
2035        
2036          <a href="https://arxiv.org/pdf/2408.03768" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
2037        
2038      
2039      
2040      
2041        <a href="https://drive.google.com/file/d/1FWyKCfvqxUaYldor781FxEAHeChyUR4z/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
2042      
2043      
2044      
2045        <a href="https://github.com/marmotlab/HDPlanner_Exp_and_Nav" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
2046      
2047      
2048      
2049      
2050    </div>
2051    
2052      
2053      
2054      
2055      
2056    
2057
2058    
2059
2060    
2061      <!-- Hidden abstract block -->
2062      <div class="abstract hidden">
2063        <p>In this paper, we introduce HDPlanner, a deep reinforcement learning (DRL) based framework designed to tackle two core and challenging tasks for mobile robots: autonomous exploration and navigation, where the robot must optimize its trajectory adaptively to achieve the task objective through continuous interactions in unknown environments. Specifically, HDPlanner relies on novel hierarchical attention networks to empower the robot to reason about its belief across multiple spatial scales and sequence collaborative decisions, where our networks decompose long-term objectives into short-term informative task assignments and informative path plannings. We further propose a contrastive learning-based joint optimization to enhance the robustness of HDPlanner. We empirically demonstrate that HDPlanner significantly outperforms state-of-the-art conventional and learning-based baselines on an extensive set of simulations, including hundreds of test maps and large-scale, complex Gazebo environments. Notably, HDPlanner achieves real-time planning with travel distances reduced by up to 35.7% compared to exploration benchmarks and by up to 16.5% than navigation benchmarks. Furthermore, we validate our approach on hardware, where it generates high-quality, adaptive trajectories in both indoor and outdoor environments, highlighting its real-world applicability without additional training.</p>
2064      </div>
2065    
2066
2067    
2068      <!-- Hidden bibtex block -->
2069      <div class="bibtex hidden">
2070        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">liang2024hdplanner</span><span class="p">,</span>
2071  <span class="na">title</span> <span class="p">=</span> <span class="s">{HDPlanner: Advancing Autonomous Deployments in Unknown Environments through Hierarchical Decision Networks}</span><span class="p">,</span>
2072  <span class="na">author</span> <span class="p">=</span> <span class="s">{Liang, Jingsong and Cao, Yuhong and Ma, Yixiao and Zhao, Hanqi and Sartoretti, Guillaume}</span><span class="p">,</span>
2073  <span class="na">
2073journal</span> <span class="p">=</span> <span class="s">{IEEE Robotics and Automation Letters}</span><span class="p">,</span>
2074  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
2075  <span class="na">publisher</span> <span class="p">=</span> <span class="s">{IEEE}</span><span class="p">,</span>
2076<span class="p">}</span></code></pre></figure>
2077      </div>
2078    
2079
2080    
2081  </div>
2082</div>
2083</li>
2084<li>
2085<div class="row">
2086  
2087    <div class="col col-sm-2 abbr">
2088      
2089        
2090          
2091          
2092
2093<figure>
2094  <picture>
2095    <!-- Auto scaling with imagemagick -->
2096    <!--
2097      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
2098      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
2099    -->
2100    
2101      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/viper-480.webp 480w,/assets/img/publication_preview/viper-800.webp 800w,/assets/img/publication_preview/viper-1400.webp 1400w," sizes="200px" type="image/webp"></source>
2102    
2103    <img src="/assets/img/publication_preview/viper.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="viper.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
2104  </picture>
2105
2106  
2107</figure>
2108
2109        
2110      
2111    </div>
2112  
2113
2114  <!-- Entry bib key -->
2115  <div id="wang2024viper" class="col-sm-8">
2116    <!-- Title -->
2117    <div class="title">ViPER: Visibility-based Pursuit-Evasion via Reinforcement Learning</div>
2118    <!-- Author -->
2119    <div class="author">
2120      
2121
2122      
2123      Yizhuo
2124            Wang*, <em>Yuhong
2125            Cao*</em>, Jimmy
2126            Chiun, Koley
2127            Subhadeep, Pham
2128            Mandy, and Guillaume
2129            Sartoretti
2130    </div>
2131
2132    <!-- Journal/Book title and date -->
2133    
2134    
2135    
2136    
2137    
2138    
2139    
2140    
2141    
2142    
2143    <div class="periodical">
2144      <em>In Conference on Robot Learning</em> ,  2024
2145    </div>
2146    <div class="periodical">
2147      
2148    </div>
2149
2150    <!-- Links/Buttons -->
2151    <div class="links">
2152      
2153      
2154        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2155      
2156      
2157      
2158        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2159      
2160      
2161      
2162        
2163          <a href="https://openreview.net/pdf?id=EPujQZWemk" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
2164        
2165      
2166      
2167      
2168        <a href="https://drive.google.com/file/d/189UFUa1BALAe8GTF2efyd195l6D2GqVH/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
2169      
2170      
2171      
2172        <a href="https://github.com/marmotlab/ViPER" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
2173      
2174      
2175      
2176      
2177    </div>
2178    
2179      
2180      
2181      
2182      
2183    
2184
2185    
2186
2187    
2188      <!-- Hidden abstract block -->
2189      <div class="abstract hidden">
2190        <p>In visibility-based pursuit-evasion tasks, a team of mobile pursuer robots with limited sensing capabilities is tasked with detecting all evaders in a multiply-connected planar environment, whose map may or may not be known to pursuers beforehand.
2191This requires tight coordination among multiple agents to ensure that the omniscient and potentially arbitrarily fast evaders are guaranteed to be detected by the pursuers.
2192Whereas existing methods typically rely on a relatively large team of agents to clear the environment, we propose ViPER, a neural solution that leverages a graph attention network to learn a coordinated yet distributed policy via multi-agent reinforcement learning (MARL).
2193We experimentally demonstrate that ViPER significantly outperforms other state-of-the-art non-learning planners, showcasing its emergent coordinated behaviors and adaptability to more challenging scenarios and various team sizes, and finally deploy its learned policies on hardware in an aerial search task.</p>
2194      </div>
2195    
2196
2197    
2198      <!-- Hidden bibtex block -->
2199      <div class="bibtex hidden">
2200        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">wang2024viper</span><span class="p">,</span>
2201  <span class="na">title</span> <span class="p">=</span> <span class="s">{ViPER: Visibility-based Pursuit-Evasion via Reinforcement Learning}</span><span class="p">,</span>
2202  <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang*, Yizhuo and Cao*, Yuhong and Chiun, Jimmy and Subhadeep, Koley and Mandy, Pham and Sartoretti, Guillaume}</span><span class="p">,</span>
2203  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Conference on Robot Learning}</span><span class="p">,</span>
2204  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span>
2205<span class="p">}</span></code></pre></figure>
2206      </div>
2207    
2208
2209    
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2211</div>
2212</li>
2213<li>
2214<div class="row">
2215  
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2217      
2218        
2219          
2220          
2221
2222<figure>
2223  <picture>
2224    <!-- Auto scaling with imagemagick -->
2225    <!--
2226      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
2227      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
2228    -->
2229    
2230      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/bandwidth-limited-exploration-480.webp 480w,/assets/img/publication_preview/bandwidth-limited-exploration-800.webp 800w,/assets/img/publication_preview/bandwidth-limited-exploration-1400.webp 1400w," sizes="200px" type="image/webp"></source>
2231    
2232    <img src="/assets/img/publication_preview/bandwidth-limited-exploration.png" class="preview z-depth-1 rounded" width="100%" height="auto" alt="bandwidth-limited-exploration.png" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
2233  </picture>
2234
2235  
2236</figure>
2237
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2239      
2240    </div>
2241  
2242
2243  <!-- Entry bib key -->
2244  <div id="ma2024privileged" class="col-sm-8">
2245    <!-- Title -->
2246    <div class="title">Privileged Reinforcement and Communication Learning for Distributed, Bandwidth-limited Multi-robot Exploration</div>
2247    <!-- Author -->
2248    <div class="author">
2249      
2250
2251      
2252      Yixiao
2253            Ma, Jingsong
2254            Liang, <em>Yuhong
2255            Cao</em>, Derek Ming Siang
2256            Tan, and Guillaume
2257            Sartoretti
2258    </div>
2259
2260    <!-- Journal/Book title and date -->
2261    
2262    
2263    
2264    
2265    
2266    
2267    
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2269    
2270    
2271    <div class="periodical">
2272      <em>In International Symposium on Distributed Autonomous Robotic Systems (DARS)</em> ,  2024
2273    </div>
2274    <div class="periodical">
2275      
2276    </div>
2277
2278    <!-- Links/Buttons -->
2279    <div class="links">
2280      
2281      
2282        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2283      
2284      
2285      
2286        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2287      
2288      
2289      
2290        
2291          <a href="https://arxiv.org/pdf/2407.20203" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
2292        
2293      
2294      
2295      
2296      
2297      
2298        <a href="https://github.com/marmotlab/Bandwidth-Limited-Multi-Robot-Exploration" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
2299      
2300      
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2304    
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2306      
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2310
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2312
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2314      <!-- Hidden abstract block -->
2315      <div class="abstract hidden">
2316        <p>Communication bandwidth is an important consideration in multi-robot exploration, where information exchange among robots is critical. While existing methods typically aim to reduce communication throughput, they either require significant computation or significantly compromise exploration efficie
2316ncy. In this work, we propose a deep reinforcement learning framework based on communication and privileged reinforcement learning to achieve a significant reduction in bandwidth consumption, while minimally sacrificing exploration efficiency. Specifically, our approach allows robots to learn to embed the most salient information from their individual belief (partial map) over the environment into fixed-sized messages. Robots then reason about their own belief as well as received messages to distributedly explore the environment while avoiding redundant work. In doing so, we employ privileged learning and learned attention mechanisms to endow the critic (i.e., teacher) network with ground truth map knowledge to effectively guide the policy (i.e., student) network during training. Compared to relevant baselines, our model allows the team to reduce communication by up to two orders of magnitude, while only sacrificing a marginal 2.4% in total travel distance, paving the way for efficient, distributed multi-robot exploration in bandwidth-limited scenarios.</p>
2317      </div>
2318    
2319
2320    
2321      <!-- Hidden bibtex block -->
2322      <div class="bibtex hidden">
2323        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">ma2024privileged</span><span class="p">,</span>
2324  <span class="na">title</span> <span class="p">=</span> <span class="s">{Privileged Reinforcement and Communication Learning for Distributed, Bandwidth-limited Multi-robot Exploration}</span><span class="p">,</span>
2325  <span class="na">author</span> <span class="p">=</span> <span class="s">{Ma, Yixiao and Liang, Jingsong and Cao, Yuhong and Tan, Derek Ming Siang and Sartoretti, Guillaume}</span><span class="p">,</span>
2326  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{International Symposium on Distributed Autonomous Robotic Systems (DARS)}</span><span class="p">,</span>
2327  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span>
2328<span class="p">}</span></code></pre></figure>
2329      </div>
2330    
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2354    
2355    <img src="/assets/img/publication_preview/i2r.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="i2r.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
2356  </picture>
2357
2358  
2359</figure>
2360
2361        
2362      
2363    </div>
2364  
2365
2366  <!-- Entry bib key -->
2367  <div id="tan2024IR2" class="col-sm-8">
2368    <!-- Title -->
2369    <div class="title">IR2: Implicit Rendezvous for Robotic Exploration Teams under Sparse Intermittent Connectivity</div>
2370    <!-- Author -->
2371    <div class="author">
2372      
2373
2374      
2375      Derek Ming Siang
2376            Tan, Yixiao
2377            Ma, Jingsong
2378            Liang, Yi Cheng
2379            Chng, <em>Yuhong
2380            Cao</em>, and Guillaume
2381            Sartoretti
2382    </div>
2383
2384    <!-- Journal/Book title and date -->
2385    
2386    
2387    
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2395    <div class="periodical">
2396      <em>In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)</em> ,  2024
2397    </div>
2398    <div class="periodical">
2399      
2400    </div>
2401
2402    <!-- Links/Buttons -->
2403    <div class="links">
2404      
2405      
2406        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2407      
2408      
2409      
2410        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2411      
2412      
2413      
2414        
2415          <a href="https://arxiv.org/pdf/2409.04730" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
2416        
2417      
2418      
2419      
2420        <a href="https://drive.google.com/file/d/1vh3jUCLaxYTk40VNZW1dhtokaL1AfrnX/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
2421      
2422      
2423      
2424        <a href="https://github.com/marmotlab/IR2-Multi-Robot-RL-Exploration" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
2425      
2426      
2427      
2428      
2429        <a href="https://ir2-explore.github.io" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Website</a>
2430      
2431    </div>
2432    
2433      
2434      
2435      
2436      
2437    
2438
2439    
2440
2441    
2442      <!-- Hidden abstract block -->
2443      <div class="abstract hidden">
2444        <p>Information sharing is critical in time-sensitive and realistic multi-robot exploration, especially for smaller robotic teams in large-scale environments where connectivity may be sparse and intermittent. Existing methods often overlook such communication constraints by assuming unrealistic global connectivity. Other works account for communication constraints (by maintaining close proximity or line of sight during information exchange), but are often inefficient. For instance, preplanned rendezvous approaches typically involve unnecessary detours resulting from poorly timed rendezvous, while pursuit-based approaches often result in short-sighted decisions due to their greedy 
2444nature. We present IR2, a deep reinforcement learning approach to information sharing for multi-robot exploration. Leveraging attention-based neural networks trained via reinforcement and curriculum learning, IR2 allows robots to effectively reason about the longer-term trade-offs between disconnecting for solo exploration and reconnecting for information sharing. In addition, we propose a hierarchical graph formulation to maintain a sparse yet informative graph, enabling our approach to scale to large-scale environments. We present simulation results in three large-scale Gazebo environments, which show that our approach yields 6.6-34.1% shorter exploration paths and significantly improved mapped area consistency among robots when compared to state-of-the-art baselines.</p>
2445      </div>
2446    
2447
2448    
2449      <!-- Hidden bibtex block -->
2450      <div class="bibtex hidden">
2451        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">tan2024IR2</span><span class="p">,</span>
2452  <span class="na">title</span> <span class="p">=</span> <span class="s">{IR2: Implicit Rendezvous for Robotic Exploration Teams under Sparse Intermittent Connectivity}</span><span class="p">,</span>
2453  <span class="na">author</span> <span class="p">=</span> <span class="s">{Tan, Derek Ming Siang and Ma, Yixiao and Liang, Jingsong and Chng, Yi Cheng and Cao, Yuhong and Sartoretti, Guillaume}</span><span class="p">,</span>
2454  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}</span><span class="p">,</span>
2455  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span>
2456<span class="p">}</span></code></pre></figure>
2457      </div>
2458    
2459
2460    
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2462</div>
2463</li>
2464<li>
2465<div class="row">
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2469        
2470          
2471          
2472
2473<figure>
2474  <picture>
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2477      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
2478      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
2479    -->
2480    
2481      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/star-480.webp 480w,/assets/img/publication_preview/star-800.webp 800w,/assets/img/publication_preview/star-1400.webp 1400w," sizes="200px" type="image/webp"></source>
2482    
2483    <img src="/assets/img/publication_preview/star.png" class="preview z-depth-1 rounded" width="100%" height="auto" alt="star.png" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
2484  </picture>
2485
2486  
2487</figure>
2488
2489        
2490      
2491    </div>
2492  
2493
2494  <!-- Entry bib key -->
2495  <div id="chiun2024star" class="col-sm-8">
2496    <!-- Title -->
2497    <div class="title">STAR: Swarm Technology for Aerial Robotics Research</div>
2498    <!-- Author -->
2499    <div class="author">
2500      
2501
2502      
2503      Jimmy
2504            Chiun, Yan Rui
2505            Tan, <em>Yuhong
2506            Cao</em>, John
2507            Tan, and Guillaume
2508            Sartoretti
2509    </div>
2510
2511    <!-- Journal/Book title and date -->
2512    
2513    
2514    
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2521    
2522    <div class="periodical">
2523      <em>The International Conference on Control, Automation, and Systems</em>,  2024
2524    </div>
2525    <div class="periodical">
2526      
2527    </div>
2528
2529    <!-- Links/Buttons -->
2530    <div class="links">
2531      
2532      
2533        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2534      
2535      
2536      
2537        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2538      
2539      
2540      
2541        
2542          <a href="https://arxiv.org/pdf/2406.16671" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
2543        
2544      
2545      
2546      
2547        <a href="https://youtu.be/8NVm2VyeYkQ?feature=shared" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
2548      
2549      
2550      
2551      
2552      
2553      
2554    </div>
2555    
2556      
2557      
2558      
2559      
2560    
2561
2562    
2563
2564    
2565      <!-- Hidden abstract block -->
2566      <div class="abstract hidden">
2567        <p>In recent years, the field of aerial robotics has witnessed significant progress, finding applications in diverse domains, including post-disaster search and rescue operations. Despite these strides, the prohibitive acquisition costs associated with deploying physical multi-UAV systems have posed challenges, impeding their widespread utilization in research endeavors. To overcome these challenges, we present STAR (Swarm Technology for Aerial Robotics Research), a framework developed explicitly to improve the accessibility of aerial swarm research experiments. Our framework introduces a swarm architecture based on the Crazyflie, a low-cost, open-source, palm-sized aerial platform, well suited for experimental swarm algorithms. To augment cost-effectiveness and mitigate the limitations of employing low-cost robots in experiments, we propose a landmark-based localization module leveraging fiducial markers. This module, also serving as a target detection module, enhances the adaptability and versatility of the framework. Additionally, collision and obstacle avoidance are implemented through velocity obstacles. The presented work strives to bridge the gap between theoretical advances and tangible implementations, thu
2567s fostering progress in the field.</p>
2568      </div>
2569    
2570
2571    
2572      <!-- Hidden bibtex block -->
2573      <div class="bibtex hidden">
2574        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">chiun2024star</span><span class="p">,</span>
2575  <span class="na">title</span> <span class="p">=</span> <span class="s">{STAR: Swarm Technology for Aerial Robotics Research}</span><span class="p">,</span>
2576  <span class="na">author</span> <span class="p">=</span> <span class="s">{Chiun, Jimmy and Tan, Yan Rui and Cao, Yuhong and Tan, John and Sartoretti, Guillaume}</span><span class="p">,</span>
2577  <span class="na">journal</span> <span class="p">=</span> <span class="s">{The International Conference on Control, Automation, and Systems}</span><span class="p">,</span>
2578  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span>
2579<span class="p">}</span></code></pre></figure>
2580      </div>
2581    
2582
2583    
2584  </div>
2585</div>
2586</li>
2587<li>
2588<div class="row">
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2590    <div class="col col-sm-2 abbr">
2591      
2592        
2593          
2594          
2595
2596<figure>
2597  <picture>
2598    <!-- Auto scaling with imagemagick -->
2599    <!--
2600      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
2601      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
2602    -->
2603    
2604      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/drl_exploration-480.webp 480w,/assets/img/publication_preview/drl_exploration-800.webp 800w,/assets/img/publication_preview/drl_exploration-1400.webp 1400w," sizes="200px" type="image/webp"></source>
2605    
2606    <img src="/assets/img/publication_preview/drl_exploration.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="drl_exploration.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
2607  </picture>
2608
2609  
2610</figure>
2611
2612        
2613      
2614    </div>
2615  
2616
2617  <!-- Entry bib key -->
2618  <div id="cao2024deep" class="col-sm-8">
2619    <!-- Title -->
2620    <div class="title">Deep Reinforcement Learning-based Large-scale Robot Exploration</div>
2621    <!-- Author -->
2622    <div class="author">
2623      
2624
2625      
2626      <em>Yuhong
2627            Cao</em>, Rui
2628            Zhao, Yizhuo
2629            Wang, Bairan
2630            Xiang, and Guillaume
2631            Sartoretti
2632    </div>
2633
2634    <!-- Journal/Book title and date -->
2635    
2636    
2637    
2638    
2639    
2640    
2641    
2642    
2643    
2644    
2645    <div class="periodical">
2646      <em>IEEE Robotics and Automation Letters</em>,  2024
2647    </div>
2648    <div class="periodical">
2649      
2650    </div>
2651
2652    <!-- Links/Buttons -->
2653    <div class="links">
2654      
2655      
2656        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2657      
2658      
2659      
2660        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2661      
2662      
2663        
2664          <a href="https://ieeexplore.ieee.org/abstract/document/10476684" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a>
2665        
2666      
2667      
2668        
2669          <a href="https://arxiv.org/pdf/2403.10833" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
2670        
2671      
2672      
2673      
2674        <a href="https://drive.google.com/file/d/1nyVJnGOX2kv8Wsm2hLWIDGGKkhXAI4Tu/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
2675      
2676      
2677      
2678        <a href="https://github.com/marmotlab/large-scale-DRL-exploration" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
2679      
2680      
2681      
2682      
2683    </div>
2684    
2685      
2686      
2687      
2688      
2689    
2690
2691    
2692
2693    
2694      <!-- Hidden abstract block -->
2695      <div class="abstract hidden">
2696        <p>In this work, we propose a deep reinforcement learning (DRL) based reactive planner to solve large-scale Lidar-based autonomous robot exploration problems in 2D action space. Our DRL-based planner allows the agent to reactively plan its exploration path by making implicit predictions about unknown areas, based on a learned estimation of the underlying transition model of the environment. To this end, our approach relies on learned attention mechanisms for their powerful ability to capture long-term dependencies at different spatial scales to reason about the robot’s entire belief over known areas. Our approach relies on ground truth information (i.e., privileged learning) to guide the environment estimation during training, as well as on a graph rarefaction algorithm, which allows models trained in small-scale environments to scale to large-scale ones. Simulation results show that our model exhibits better exploration efficie
2696ncy (12% in path length, 6% in makespan) and lower planning time (60%) than the state-of-the-art planners in a 130mx100m benchmark scenario. We also validate our learned model on hardware.</p>
2697      </div>
2698    
2699
2700    
2701      <!-- Hidden bibtex block -->
2702      <div class="bibtex hidden">
2703        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">cao2024deep</span><span class="p">,</span>
2704  <span class="na">title</span> <span class="p">=</span> <span class="s">{Deep Reinforcement Learning-based Large-scale Robot Exploration}</span><span class="p">,</span>
2705  <span class="na">author</span> <span class="p">=</span> <span class="s">{Cao, Yuhong and Zhao, Rui and Wang, Yizhuo and Xiang, Bairan and Sartoretti, Guillaume}</span><span class="p">,</span>
2706  <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Robotics and Automation Letters}</span><span class="p">,</span>
2707  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
2708  <span class="na">publisher</span> <span class="p">=</span> <span class="s">{IEEE}</span>
2709<span class="p">}</span></code></pre></figure>
2710      </div>
2711    
2712
2713    
2714  </div>
2715</div>
2716</li>
2717</ol>
2718<h2 class="bibliography">2023</h2>
2719<ol class="bibliography">
2720<li>
2721<div class="row">
2722  
2723    <div class="col col-sm-2 abbr">
2724      
2725        
2726          
2727          
2728
2729<figure>
2730  <picture>
2731    <!-- Auto scaling with imagemagick -->
2732    <!--
2733      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
2734      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
2735    -->
2736    
2737      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/mapless_nav-480.webp 480w,/assets/img/publication_preview/mapless_nav-800.webp 800w,/assets/img/publication_preview/mapless_nav-1400.webp 1400w," sizes="200px" type="image/webp"></source>
2738    
2739    <img src="/assets/img/publication_preview/mapless_nav.png" class="preview z-depth-1 rounded" width="100%" height="auto" alt="mapless_nav.png" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
2740  </picture>
2741
2742  
2743</figure>
2744
2745        
2746      
2747    </div>
2748  
2749
2750  <!-- Entry bib key -->
2751  <div id="liang2023context" class="col-sm-8">
2752    <!-- Title -->
2753    <div class="title">Context-Aware Deep Reinforcement Learning for Autonomous Robotic Navigation in Unknown Area</div>
2754    <!-- Author -->
2755    <div class="author">
2756      
2757
2758      
2759      Jingsong
2760            Liang*, Zhichen
2761            Wang*, <em>Yuhong
2762            Cao*</em>, Jimmy
2763            Chiun, Mengqi
2764            Zhang, and Guillaume Adrien
2765            Sartoretti
2766    </div>
2767
2768    <!-- Journal/Book title and date -->
2769    
2770    
2771    
2772    
2773    
2774    
2775    
2776    
2777    
2778    
2779    <div class="periodical">
2780      <em>In Conference on Robot Learning</em> ,  2023
2781    </div>
2782    <div class="periodical">
2783      
2784    </div>
2785
2786    <!-- Links/Buttons -->
2787    <div class="links">
2788      
2789      
2790        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2791      
2792      
2793      
2794        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2795      
2796      
2797        
2798          <a href="https://proceedings.mlr.press/v229/liang23a.html" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a>
2799        
2800      
2801      
2802        
2803          <a href="https://proceedings.mlr.press/v229/liang23a/liang23a.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
2804        
2805      
2806      
2807      
2808        <a href="https://drive.google.com/file/d/1r-5TXA6zOAJUfI9E8I_48SoT3WXgRSzu/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
2809      
2810      
2811      
2812        <a href="https://github.com/marmotlab/Context_Aware_Navigation" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
2813      
2814      
2815      
2816      
2817    </div>
2818    
2819      
2820      
2821      
2822      
2823    
2824
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2826
2827    
2828      <!-- Hidden abstract block -->
2829      <div class="abstract hidden">
2830        <p>Mapless navigation refers to a challenging task where a mobile robot must rapidly navigate to a predefined destination using its partial knowledge of the environment, which is updated online along the way, instead of a prior map of the environment. Inspired by the recent developments in deep reinforcement learning (DRL), we propose a learning-based framework for mapless navigation, which employs a context-aware policy network to achieve efficient decision-making (ie, maximize the likelihood of finding the shortest route towards the target destination), especially in complex and large-scale environments. Specifically, our robot learns to form a context of its belief over the entire known area, which it uses to reason about long-term efficiency and sequence show-term movements. Additionally, we propose a graph rarefaction algorithm to enable more efficient decision-making in large-scale applications. We empirically demonstrate that our approach reduces average travel time by up to 61.4%  and average planning time by up to 88.2%  compared to benchmark planners (D* lite and BIT) on hundreds of test scenarios. We also validate our approach both in high-fidelity Gazebo simulations as well as on hardware, highlighting its promising applicability in the real world without further training/tuning.</p>
2831      </div>
2832    
2833
2834    
2835      <!-- Hidden bibtex block -->
2836      <div class="bibtex hidden">
2837        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">liang2023context</span><span class="p">,</span>
2838  <span class="na">title</span> <span class="p">=</span> <span class="s">{Context-Aware Deep Reinforcement Learning for Autonomous Robotic Navigation in Unknown Area}</span><span class="p">,</span>
2839  <span class="na">author</span> <span class="p">=</span> <span class="s">{Liang*, Jingsong and Wang*, Zhichen and Cao*, Yuhong and Chiun, Jimmy and Zhang, Mengqi and Sartoretti, Guillaume Adrien}</span><span class="p">,</span>
2840  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Conference on Robot Learning}</span><span class="p">,</span>
2841  <span class="na">pages</span> <span class="p">=</span> <span class="s">{1425--1436}</span><span class="p">,</span>
2842  <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span>
2843  <span class="na">organization</span> <span class="p">=</span> <span class="s">{PMLR}</span>
2844<span class="p">}</span></code></pre></figure>
2845      </div>
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2850</div>
2851</li>
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2853<div class="row">
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2867    -->
2868    
2869      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/maipp-480.webp 480w,/assets/img/publication_preview/maipp-800.webp 800w,/assets/img/publication_preview/maipp-1400.webp 1400w," sizes="200px" type="image/webp"></source>
2870    
2871    <img src="/assets/img/publication_preview/maipp.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="maipp.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
2872  </picture>
2873
2874  
2875</figure>
2876
2877        
2878      
2879    </div>
2880  
2881
2882  <!-- Entry bib key -->
2883  <div id="yang2023intent" class="col-sm-8">
2884    <!-- Title -->
2885    <div class="title">Intent-based Deep Reinforcement Learning for Multi-agent Informative Path Planning</div>
2886    <!-- Author -->
2887    <div class="author">
2888      
2889
2890      
2891      Tianze
2892            Yang, <em>Yuhong
2893            Cao</em>, and Guillaume
2894            Sartoretti
2895    </div>
2896
2897    <!-- Journal/Book title and date -->
2898    
2899    
2900    
2901    
2902    
2903    
2904    
2905    
2906    
2907    
2908    <div class="periodical">
2909      <em>In 2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)</em> ,  2023
2910    </div>
2911    <div class="periodical">
2912      
2913    </div>
2914
2915    <!-- Links/Buttons -->
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2919        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
2920      
2921      
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2923        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
2924      
2925      
2926        
2927          <a href="https://ieeexplore.ieee.org/abstract/document/10416797" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a>
2928        
2929      
2930      
2931        
2932          <a href="https://arxiv.org/pdf/2303.05351" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
2933        
2934      
2935      
2936      
2937        <a href="https://drive.google.com/file/d/1pUg6oZQGTELy4IIGfedvMd04r85GSme6/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
2938      
2939      
2940      
2941        <a href="https://github.com/marmotlab/MAIPP/tree/trajectory_intent" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
2942      
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2947    
2948      
2949      
2950      
2951      
2952    
2953
2954    
2955
2956    
2957      <!-- Hidden abstract block -->
2958      <div class="abstract hidden">
2959        <p>In multi-agent informative path planning (MAIPP), agents must collectively construct a global belief map of an underlying distribution of interest (e.g., gas concentration, light intensity, or pollution levels) over a given domain, based on measurements taken along their trajectory. They must frequently replan their path to balance the distributed exploration of new areas and the collective, meticulous exploitation of known high-interest areas, to maximize the information gained within a predefined budget (e.g., path length or working time). A common approach to achieving such cooperation relies on planning the agents’ paths reactively, conditioned on other agents’ future actions. However, as the agent’s belief is updated continuously, these predicted future actions may not end up being the ones executed by agents, introducing a form of noise/inaccuracy in the system and often decreasing performance. In this work, we propose a decentralized deep reinforcement learning (DRL) approach to MAIPP, which relies on an attention-based neural network, where agents optimize long-term individual and cooperative objectives by explicitly sharing their intent (i.e., medium-/long-term future positions distribution, obtained from their individual policy) in a reactive, asynchronous manner. That is, in our work, intent sharing allows agents to learn to claim/avoid broader areas of the world. Moreover, since our approach relies on learned attention over these shared intents, agents are able to learn to recognize the useful portion(s) of these (imperfect) predictions to maximize cooperation even in the presence of imperfect information. Our comparison experiments demonstrate the performance of our approach compared to its variants and high-quality baselines over a large set of MAIPP simulations.</p>
2960      </div>
2961    
2962
2963    
2964      <!-- Hidden bibtex block -->
2965      <div class="bibtex hidden">
2966        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">yang2023intent</span><span class="p">,</span>
2967  <span class="na">title</span> <span class="p">=</span> <span class="s">{Intent-based Deep Reinforcement Learning for Multi-agent Informative Path Planning}</span><span class="p">,</span>
2968  <span class="na">author</span> <span class="p">=</span> <span class="s">{Yang, Tianze and Cao, Yuhong and Sartoretti, Guillaume}</span><span class="p">,</span>
2969  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)}</span><span class="p">,</span>
2970  <span class="na">pages</span> <span class="p">=</span> <span class="s">{71--77}</span><span class="p">,</span>
2971  <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span>
2972  <span class="na">organization</span> <span class="p">=</span> <span class="s">{IEEE}</span>
2973<span class="p">}</span></code></pre></figure>
2974      </div>
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2977    
2978  </div>
2979</div>
2980</li>
2981<li>
2982<div class="row">
2983  
2984    <div class="col col-sm-2 abbr">
2985      
2986        
2987          
2988          
2989
2990<figure>
2991  <picture>
2992    <!-- Auto scaling with imagemagick -->
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2994      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
2995      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
2996    -->
2997    
2998      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/stamp-480.webp 480w,/assets/img/publication_preview/stamp-800.webp 800w,/assets/img/publication_preview/stamp-1400.webp 1400w," sizes="200px" type="image/webp"></source>
2999    
3000    <img src="/assets/img/publication_preview/stamp.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="stamp.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
3001  </picture>
3002
3003  
3004</figure>
3005
3006        
3007      
3008    </div>
3009  
3010
3011  <!-- Entry bib key -->
3012  <div id="wang2023spatio" class="col-sm-8">
3013    <!-- Title -->
3014    <div class="title">Spatio-temporal attention network for persistent monitoring of multiple mobile targets</div>
3015    <!-- Author -->
3016    <div class="author">
3017      
3018
3019      
3020      Yizhuo
3021            Wang, Yutong
3022            Wang, <em>Yuhong
3023            Cao</em>, and Guillaume
3024            Sartoretti
3025    </div>
3026
3027    <!-- Journal/Book title and date -->
3028    
3029    
3030    
3031    
3032    
3033    
3034    
3035    
3036    
3037    
3038    <div class="periodical">
3039      <em>In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (I
3039ROS)</em> ,  2023
3040    </div>
3041    <div class="periodical">
3042      
3043    </div>
3044
3045    <!-- Links/Buttons -->
3046    <div class="links">
3047      
3048      
3049        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
3050      
3051      
3052      
3053        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
3054      
3055      
3056        
3057          <a href="https://ieeexplore.ieee.org/abstract/document/10341674" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a>
3058        
3059      
3060      
3061        
3062          <a href="https://arxiv.org/pdf/2303.06350" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
3063        
3064      
3065      
3066      
3067        <a href="https://drive.google.com/file/d/1OiOg9guiJnos-IaYwK5cLVLadCmx741T/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
3068      
3069      
3070      
3071        <a href="https://github.com/marmotlab/STAMP" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
3072      
3073      
3074      
3075      
3076    </div>
3077    
3078      
3079      
3080      
3081      
3082    
3083
3084    
3085
3086    
3087      <!-- Hidden abstract block -->
3088      <div class="abstract hidden">
3089        <p>This work focuses on the persistent monitoring problem, where a set of targets moving based on an unknown model must be monitored by an autonomous mobile robot with a limited sensing range. To keep each target’s position estimate as accurate as possible, the robot needs to adaptively plan its path to (re-)visit all the targets and update its belief from measurements collected along the way. In doing so, the main challenge is to strike a balance between exploitation, i.e., re-visiting previously-located targets, and exploration, i.e., finding new targets or re-acquiring lost ones.
3090Encouraged by recent advances in deep reinforcement learning, we introduce an attention-based neural solution to the persistent monitoring problem, where the agent can learn the inter-dependencies between targets, i.e., their spatial and temporal correlations, conditioned on past measurements.
3091This endows the agent with the ability to determine which target, time, and location to attend to across multiple scales, which we show also helps relax the usual limitations of a finite target set with prior positional information.
3092We experimentally demonstrate that our method outperforms other baselines in terms of number of targets visits and average estimation error in complex environments.
3093Finally, we implement and validate our model in a drone-based simulation experiment to monitor mobile ground targets in a high-fidelity simulator.</p>
3094      </div>
3095    
3096
3097    
3098      <!-- Hidden bibtex block -->
3099      <div class="bibtex hidden">
3100        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">wang2023spatio</span><span class="p">,</span>
3101  <span class="na">title</span> <span class="p">=</span> <span class="s">{Spatio-temporal attention network for persistent monitoring of multiple mobile targets}</span><span class="p">,</span>
3102  <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Yizhuo and Wang, Yutong and Cao, Yuhong and Sartoretti, Guillaume}</span><span class="p">,</span>
3103  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}</span><span class="p">,</span>
3104  <span class="na">pages</span> <span class="p">=</span> <span class="s">{3903--3910}</span><span class="p">,</span>
3105  <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span>
3106  <span class="na">organization</span> <span class="p">=</span> <span class="s">{IEEE}</span>
3107<span class="p">}</span></code></pre></figure>
3108      </div>
3109    
3110
3111    
3112  </div>
3113</div>
3114</li>
3115<li>
3116<div class="row">
3117  
3118    <div class="col col-sm-2 abbr">
3119      
3120        
3121          
3122          
3123
3124<figure>
3125  <picture>
3126    <!-- Auto scaling with imagemagick -->
3127    <!--
3128      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
3129      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
3130    -->
3131    
3132      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/ariadne-480.webp 480w,/assets/img/publication_preview/ariadne-800.webp 800w,/assets/img/publication_preview/ariadne-1400.webp 1400w," sizes="200px" type="image/webp"></source>
3133    
3134    <img src="/assets/img/publication_preview/ariadne.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="ariadne.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
3135  </picture>
3136
3137  
3138</figure>
3139
3140        
3141      
3142    </div>
3143  
3144
3145  <!-- Entry bib key -->
3146  <div id="cao2023ariadne" class="col-sm-8">
3147    <!-- Title -->
3148    <div class="title">
3148Ariadne: A reinforcement learning approach using attention-based deep networks for exploration</div>
3149    <!-- Author -->
3150    <div class="author">
3151      
3152
3153      
3154      <em>Yuhong
3155            Cao</em>, Tianxiang
3156            Hou, Yizhuo
3157            Wang, Xian
3158            Yi, and Guillaume
3159            Sartoretti
3160    </div>
3161
3162    <!-- Journal/Book title and date -->
3163    
3164    
3165    
3166    
3167    
3168    
3169    
3170    
3171    
3172    
3173    <div class="periodical">
3174      <em>In 2023 IEEE International Conference on Robotics and Automation (ICRA)</em> ,  2023
3175    </div>
3176    <div class="periodical">
3177      
3178    </div>
3179
3180    <!-- Links/Buttons -->
3181    <div class="links">
3182      
3183      
3184        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
3185      
3186      
3187      
3188        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
3189      
3190      
3191        
3192          <a href="https://ieeexplore.ieee.org/abstract/document/10160565" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a>
3193        
3194      
3195      
3196        
3197          <a href="https://arxiv.org/pdf/2301.11575" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
3198        
3199      
3200      
3201      
3202        <a href="https://drive.google.com/file/d/1h0p8d9Ry96FJGYI0mK1aNYicDCS7UPzs/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
3203      
3204      
3205      
3206        <a href="https://github.com/marmotlab/ARiADNE" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
3207      
3208      
3209      
3210      
3211    </div>
3212    
3213      
3214      
3215      
3216      
3217    
3218
3219    
3220
3221    
3222      <!-- Hidden abstract block -->
3223      <div class="abstract hidden">
3224        <p>In autonomous robot exploration tasks, a mobile robot needs to actively explore and map an unknown environment as fast as possible. Since the environment is being revealed during exploration, the robot needs to frequently re-plan its path online, as new information is acquired by onboard sensors and used to update its partial map. While state-of-the-art exploration planners are frontier- and sampling-based, encouraged by the recent development in deep reinforcement learning (DRL), we propose ARiADNE, an attention-based neural approach to obtain real-time, non-myopic path planning for autonomous exploration. ARiADNE is able to learn dependencies at multiple spatial scales between areas of the agent’s partial map, and implicitly predict potential gains associated with exploring those areas. This allows the agent to sequence movement actions that balance the natural trade-off between exploitation/refinement of the map in known areas and exploration of new areas. We experimentally demonstrate that our method outperforms both learning and non-learning state-of-the-art baselines in terms of average trajectory length to complete exploration in hundreds of simplified 2D indoor scenarios. We further validate our approach in high-fidelity Robot Operating System (ROS) simulations, where we consider a real sensor model and a realistic low-level motion controller, toward deployment on real robots.</p>
3225      </div>
3226    
3227
3228    
3229      <!-- Hidden bibtex block -->
3230      <div class="bibtex hidden">
3231        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">cao2023ariadne</span><span class="p">,</span>
3232  <span class="na">title</span> <span class="p">=</span> <span class="s">{Ariadne: A reinforcement learning approach using attention-based deep networks for exploration}</span><span class="p">,</span>
3233  <span class="na">author</span> <span class="p">=</span> <span class="s">{Cao, Yuhong and Hou, Tianxiang and Wang, Yizhuo and Yi, Xian and Sartoretti, Guillaume}</span><span class="p">,</span>
3234  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{2023 IEEE International Conference on Robotics and Automation (ICRA)}</span><span class="p">,</span>
3235  <span class="na">pages</span> <span class="p">=</span> <span class="s">{10219--10225}</span><span class="p">,</span>
3236  <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span>
3237  <span class="na">organization</span> <span class="p">=</span> <span class="s">{IEEE}</span>
3238<span class="p">}</span></code></pre></figure>
3239      </div>
3240    
3241
3242    
3243  </div>
3244</div>
3245</li>
3246</ol>
3247<h2 class="bibliography">2022</h2>
3248<ol class="bibliography">
3249<li>
3250<div class="row">
3251  
3252    <div class="col col-sm-2 abbr">
3253      
3254        
3255          
3256          
3257
3258<figure>
3259  <picture>
3260    <!-- Auto scaling with imagemagick -->
3261    <!--
3262      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
3263      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
3264    -->
3265    
3266      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/catnipp-480.webp 480w,/assets/img/publication_preview/catnipp-800.webp 800w,/assets/img/publication_preview/catnipp-1400.webp 1400w," sizes="200px" type="image/webp"></source>
3267    
3268    <img src="/assets/img/publication_preview/catnipp.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="catnipp.gif" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
3269  </picture>
3270
3271  
3272</figure>
3273
3274        
3275      
3276    </div>
3277  
3278
3279  <!-- Entry bib key -->
3280  <div id="cao2023catnipp" class="col-sm-8">
3281    <!-- Title -->
3282    <div class="title">CAtNIPP: Context-aware attention-based network for informative path planning</
3282div>
3283    <!-- Author -->
3284    <div class="author">
3285      
3286
3287      
3288      <em>Yuhong
3289            Cao</em>, Yizhuo
3290            Wang, Apoorva
3291            Vashisth, Haolin
3292            Fan, and Guillaume Adrien
3293            Sartoretti
3294    </div>
3295
3296    <!-- Journal/Book title and date -->
3297    
3298    
3299    
3300    
3301    
3302    
3303    
3304    
3305    
3306    
3307    <div class="periodical">
3308      <em>In Conference on Robot Learning</em> ,  2022
3309    </div>
3310    <div class="periodical">
3311      
3312    </div>
3313
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3315    <div class="links">
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3321      
3322        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
3323      
3324      
3325        
3326          <a href="https://proceedings.mlr.press/v205/cao23b.html" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a>
3327        
3328      
3329      
3330        
3331          <a href="https://proceedings.mlr.press/v205/cao23b/cao23b.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
3332        
3333      
3334      
3335      
3336        <a href="https://drive.google.com/file/d/1yo5TrJCzEUE65jNeEgIOP_ON7uTVZtPv/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
3337      
3338      
3339      
3340        <a href="https://github.com/marmotlab/CAtNIPP" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
3341      
3342      
3343      
3344      
3345    </div>
3346    
3347      
3348      
3349      
3350      
3351    
3352
3353    
3354
3355    
3356      <!-- Hidden abstract block -->
3357      <div class="abstract hidden">
3358        <p>Informative path planning (IPP) is an NP-hard problem, which aims at planning a path allowing an agent to build an accurate belief about a quantity of interest throughout a given search domain, within constraints on resource budget (e.g., path length for robots with limited battery life). IPP requires frequent online replanning as this belief is updated with every new measurement (i.e., adaptive IPP), while balancing short-term exploitation and longer-term exploration to avoid suboptimal, myopic behaviors. Encouraged by the recent developments in deep reinforcement learning, we introduce CAtNIPP, a fully reactive, neural approach to the adaptive IPP problem. CAtNIPP relies on self-attention for its powerful ability to capture 
3359dependencies in data at multiple spatial scales. Specifically, our agent learns to form a context of its belief over the entire domain, which it uses to sequence local movement decisions 
3360that optimize short- and longer-term search objectives. We experimentally demonstrate that CAtNIPP significantly outperforms state-of-the-art non-learning IPP solvers in terms of solution quality and computing time once trained, and present experimental results on hardware.</p>
3361      </div>
3362    
3363
3364    
3365      <!-- Hidden bibtex block -->
3366      <div class="bibtex hidden">
3367        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">cao2023catnipp</span><span class="p">,</span>
3368  <span class="na">title</span> <span class="p">=</span> <span class="s">{CAtNIPP: Context-aware attention-based network for informative path planning}</span><span class="p">,</span>
3369  <span class="na">author</span> <span class="p">=</span> <span class="s">{Cao, Yuhong and Wang, Yizhuo and Vashisth, Apoorva and Fan, Haolin and Sartoretti, Guillaume Adrien}</span><span class="p">,</span>
3370  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Conference on Robot Learning}</span><span class="p">,</span>
3371  <span class="na">pages</span> <span class="p">=</span> <span class="s">{1928--1937}</span><span class="p">,</span>
3372  <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span class="p">,</span>
3373  <span class="na">organization</span> <span class="p">=</span> <span class="s">{PMLR}</span>
3374<span class="p">}</span></code></pre></figure>
3375      </div>
3376    
3377
3378    
3379  </div>
3380</div>
3381</li>
3382<li>
3383<div class="row">
3384  
3385    <div class="col col-sm-2 abbr">
3386      
3387        
3388          
3389          
3390
3391<figure>
3392  <picture>
3393    <!-- Auto scaling with imagemagick -->
3394    <!--
3395      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
3396      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
3397    -->
3398    
3399      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/distributed_learning_review-480.webp 480w,/assets/img/publication_preview/distributed_learning_review-800.webp 800w,/assets/img/publication_preview/distributed_learning_review-1400.webp 1400w," sizes="200px" type="image/webp"></source>
3400    
3401    <img src="/assets/img/publication_preview/distributed_learning_review.jpg" class="preview z-depth-1 rounded" width="100%" height="auto" alt="distributed_learning_review.jpg" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
3402  </picture>
3403
3404  
3405</figure>
3406
3407        
3408      
3409    </div>
3410  
3411
3412  <!-- Entry bib key -->
3413  <div id="wang2022distributed" class="col-sm-8">
3414    <!-- Title -->
3415    <div class="title">Distributed reinforcement learning for robot teams: A review</div>
3416    <!-- Author -->
3417    <div class="author">
3418      
3419
3420      
3421      Yutong
3422            Wang, Mehul
3423            Damani, Pamela
3424            Wang, <em>Yuhong
3425            Cao</em>, and Guillaume
3426            Sartoretti
3427    </div>
3428
3429    <!-- Journal/Book title and date -->
3430    
3431    
3432    
3433    
3434    
3435    
3436    
3437    
3438    
3439    
3440    <div class="periodical">
3441      <em>Current Robotics Reports</em>,  2022
3442    </div>
3443    <div class="periodical">
3444      
3445    </div>
3446
3447    <!-- Links/Buttons -->
3448    <div class="links">
3449      
3450      
3451        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
3452      
3453      
3454      
3455        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
3456      
3457      
3458        
3459          <a href="https://link.springer.com/article/10.1007/s43154-022-00091-8" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a>
3460        
3461      
3462      
3463        
3464          <a href="https://arxiv.org/pdf/2204.03516" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
3465        
3466      
3467      
3468      
3469      
3470      
3471      
3472      
3473      
3474    </div>
3475    
3476      
3477      
3478      
3479      
3480    
3481
3482    
3483
3484    
3485      <!-- Hidden abstract block -->
3486      <div class="abstract hidden">
3487        <p>Recent advances in sensing, actuation, and computation have opened the
3488door to multi-robot systems consisting of hundreds/thousands of robots, with promising applications
3489to automated manufacturing, disaster relief, harvesting, last-mile delivery, port/airport operations,
3490or search and rescue. The community has leveraged model-free multi-agent reinfor
3490cement learning
3491(MARL) to devise efficient, scalable controllers for multi-robot systems (MRS). This review aims
3492to provide an analysis of the state-of-the-art in distributed MARL for multi-robot cooperation. Decentralized MRS face fundamental challenges, such as non-stationarity and
3493partial observability. Building upon the “centralized training, decentralized execution” paradigm,
3494recent MARL approaches include independent learning, centralized critic, value decomposition, and
3495communication learning approaches. Cooperative behaviors are demonstrated through AI benchmarks and fundamental real-world robotic capabilities such as multi-robot motion/path planning.  This survey reports the challenges surrounding decentralized model-free MARL
3496for multi-robot cooperation and existing classes of approaches. We present benchmarks
3497and robotic applications along with a discussion on current open avenues for research.</p>
3498      </div>
3499    
3500
3501    
3502      <!-- Hidden bibtex block -->
3503      <div class="bibtex hidden">
3504        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">wang2022distributed</span><span class="p">,</span>
3505  <span class="na">title</span> <span class="p">=</span> <span class="s">{Distributed reinforcement learning for robot teams: A review}</span><span class="p">,</span>
3506  <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Yutong and Damani, Mehul and Wang, Pamela and Cao, Yuhong and Sartoretti, Guillaume}</span><span class="p">,</span>
3507  <span class="na">journal</span> <span class="p">=</span> <span class="s">{Current Robotics Reports}</span><span class="p">,</span>
3508  <span class="na">volume</span> <span class="p">=</span> <span class="s">{3}</span><span class="p">,</span>
3509  <span class="na">number</span> <span class="p">=</span> <span class="s">{4}</span><span class="p">,</span>
3510  <span class="na">pages</span> <span class="p">=</span> <span class="s">{239--257}</span><span class="p">,</span>
3511  <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span class="p">,</span>
3512  <span class="na">publisher</span> <span class="p">=</span> <span class="s">{Springer}</span>
3513<span class="p">}</span></code></pre></figure>
3514      </div>
3515    
3516
3517    
3518  </div>
3519</div>
3520</li>
3521<li>
3522<div class="row">
3523  
3524    <div class="col col-sm-2 abbr">
3525      
3526        
3527          
3528          
3529
3530<figure>
3531  <picture>
3532    <!-- Auto scaling with imagemagick -->
3533    <!--
3534      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
3535      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
3536    -->
3537    
3538      <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/DAN-480.webp 480w,/assets/img/publication_preview/DAN-800.webp 800w,/assets/img/publication_preview/DAN-1400.webp 1400w," sizes="200px" type="image/webp"></source>
3539    
3540    <img src="/assets/img/publication_preview/DAN.jpg" class="preview z-depth-1 rounded" width="100%" height="auto" alt="DAN.jpg" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();">
3541  </picture>
3542
3543  
3544</figure>
3545
3546        
3547      
3548    </div>
3549  
3550
3551  <!-- Entry bib key -->
3552  <div id="cao2022dan" class="col-sm-8">
3553    <!-- Title -->
3554    <div class="title">Dan: Decentralized attention-based neural network for the minmax multiple traveling salesman problem</div>
3555    <!-- Author -->
3556    <div class="author">
3557      
3558
3559      
3560      <em>Yuhong
3561            Cao</em>, Zhanhong
3562            Sun, and Guillaume
3563            Sartoretti
3564    </div>
3565
3566    <!-- Journal/Book title and date -->
3567    
3568    
3569    
3570    
3571    
3572    
3573    
3574    
3575    
3576    
3577    <div class="periodical">
3578      <em>In International Symposium on Distributed Autonomous Robotic Systems</em> ,  2022
3579    </div>
3580    <div class="periodical">
3581      
3582    </div>
3583
3584    <!-- Links/Buttons -->
3585    <div class="links">
3586      
3587        <a class="award btn btn-sm z-depth-0" role="button">Awareded</a>
3588      
3589      
3590        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
3591      
3592      
3593      
3594        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
3595      
3596      
3597        
3598          <a href="https://link.springer.com/chapter/10.1007/978-3-031-51497-5_15" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a>
3599        
3600      
3601      
3602        
3603          <a href="https://arxiv.org/pdf/2109.04205" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a>
3604        
3605      
3606      
3607      
3608        <a href="https://drive.google.com/file/d/14oUQ-Ap5SYWLJPfWagcbmvhejsfU3JwA/view?usp=sharing" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a>
3609      
3610      
3611      
3612        <a href="https://github.com/marmotlab/DAN" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
3613      
3614      
3615      
3616      
3617    </div>
3618    
3619      
3620      
3621      
3622      
3623    
3624
3625    
3626      <!-- Hidden Award block -->
3627      <div class="award hidden d-print-inline">
3628        <p></p>
3629<p>Best student paper</p>
3630
3631      </div>
3632    
3633
3634    
3635      <!-- Hidden abstract block -->
3636      <div class="abstract hidden">
3637        <p>The multiple traveling salesman problem (mTSP) is a well-known NP-hard problem with numerous real-world applications. In particular, this work addresses MinMax mTSP, where the objective is to minimize the max tour length among all agents. Many robotic deployments require recomputing potentially large mTSP instances frequently, making the natural trade-off between computing time and solution quality of great importance. However, exact and heuristic algorithms become inefficient as the number of cities increases, due to their computational complexity. Encouraged by the recent developments in deep reinforcement learning (dRL), this work approaches the mTSP as a cooperative task and introduces DAN, a decentralized attention-based neural method that aims at tackling this key trade-off. In DAN, agents learn fully decentralized policies to collaboratively construct a tour, by predicting each other’s future decisions. Our model relies on attention mechanisms and is trained using multi-agent RL with parameter sharing, providing natural scalability to the numbers of agents and cities. Our experimental results on 
3637small- to large-scale mTSP instances (50 to 1000 cities and 5 to 20 agents) show that DAN is able to match or outperform state-of-the-art solvers while keeping planning times low. In particular, given the same computation time budget, DAN outperforms all conventional and dRL-based baselines on larger-scale instances (more than 100 cities, more than 5 agents), and exhibits enhanced agent collaboration.</p>
3638      </div>
3639    
3640
3641    
3642      <!-- Hidden bibtex block -->
3643      <div class="bibtex hidden">
3644        <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">cao2022dan</span><span class="p">,</span>
3645  <span class="na">title</span> <span class="p">=</span> <span class="s">{Dan: Decentralized attention-based neural network for the minmax multiple traveling salesman problem}</span><span class="p">,</span>
3646  <span class="na">author</span> <span class="p">=</span> <span class="s">{Cao, Yuhong and Sun, Zhanhong and Sartoretti, Guillaume}</span><span class="p">,</span>
3647  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{International Symposium on Distributed Autonomous Robotic Systems}</span><span class="p">,</span>
3648  <span class="na">pages</span> <span class="p">=</span> <span class="s">{202--215}</span><span class="p">,</span>
3649  <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span class="p">,</span>
3650  <span class="na">organization</span> <span class="p">=</span> <span class="s">{Springer}</span>
3651<span class="p">}</span></code></pre></figure>
3652      </div>
3653    
3654
3655    
3656  </div>
3657</div>
3658</li>
3659</ol>
3660
3661</div>
3662
3663  </article>
3664
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3666
3667  
3668</div>
3669
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3672
3673    <!-- Footer -->
3674    
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3676    <div class="container mt-0">
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3678      Yuhong
3679      
3680      Cao. Powered by <a href="https://jekyllrb.com/" target="_blank" rel="external nofollow noopener">Jekyll</a> with <a href="https://github.com/alshedivat/al-folio" rel="external nofollow noopener" target="_blank">al-folio</a> theme.
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3784  <!-- Scrolling Progress Bar -->
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3785<script type="text/javascript">
3786    /*
3787     * This JavaScript code has been adapted from the article
3788     * https://css-tricks.com/reading-position-indicator/ authored by Pankaj Parashar,
3789     * published on the website https://css-tricks.com on the 7th of May, 2014.
3790     * Couple of changes were made to the original code to make it compatible
3791     * with the `al-foio` theme.
3792     */
3793    const progressBar = $('#progress');
3794    /*
3795     * We set up the bar after all elements are done loading.
3796     * In some cases, if the images in the page are larger than the intended
3797     * size they'll have on the page, they'll be resized via CSS to accomodate
3798     * the desired size. This mistake, however, breaks the computations as the
3799     * scroll size is computed as soon as the elements finish loading.
3800     * To account for this, a minimal delay was introduced before computing the
3801     * values.
3802     */
3803    window.onload = function () {
3804      setTimeout(progressBarSetup, 50);
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3806    /*
3807     * We set up the bar according to the browser.
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3809     * Otherwise, we resize the bar thru CSS styling
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3820        $(document).on('scroll', resizeProgressBar);
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3822      }
3823    }
3824    /*
3825     * The vertical scroll position is the same as the number of pixels that
3826     * are hidden from view above the scrollable area. Thus, a value > 0 is
3827     * how much the user has scrolled from the top
3828     */
3829    function getCurrentScrollPosition() {
3830      return $(window).scrollTop();
3831    }
3832
3833    function initializeProgressElement() {
3834      let navbarHeight = $('#navbar').outerHeight(true);
3835      $('body').css({ 'padding-top': navbarHeight });
3836      $('progress-container').css({ 'padding-top': navbarHeight });
3837      progressBar.css({ top: navbarHeight });
3838      progressBar.attr({
3839        max: getDistanceToScroll(),
3840        value: getCurrentScrollPosition(),
3841      });
3842    }
3843    /*
3844     * The offset between the html document height and the browser viewport
3845     * height will be greater than zero if vertical scroll is possible.
3846     * This is the distance the user can scroll
3847     */
3848    function getDistanceToScroll() {
3849      return $(document).height() - $(window).height();
3850    }
3851
3852    function resizeProgressBar() {
3853      progressBar.css({ width: getWidthPercentage() + '%' });
3854    }
3855    // The scroll ratio equals the percentage to resize the bar
3856    function getWidthPercentage() {
3857      return (getCurrentScrollPosition() / getDistanceToScroll()) * 100;
3858    }
3859  </script>
3859
3860
3861
3862    
3863
3864    
3865
3866    
3867
3868    
3869  
3869<script src="/assets/js/vanilla-back-to-top.min.js?f40d453793ff4f64e238e420181a1d17"></script>
3869
3870  
3870<script>
3871    addBackToTop();
3872  </script>
3872
3873
3874
3875    
3876
3877    
3877<script src="/assets/js/shortcut-key.js"></script>
3877
3878  </body>
3879</html>

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