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203      
204        Yuhong Cao
205      
206    </h1>
207    <p class="desc">Postdoc @ NUS ME</p>
208  </header>
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247<p>I am a postdoc fellow at National University of Singapore, Department of Mechanical Engineering, <a href="https://www.marmotlab.org/" rel="external nofollow noopener" target="_blank">MARMot Lab</a>. I did my PhD in NUS under the supervision of <a href="https://www.marmotlab.org/bio.html" rel="external nofollow noopener" target="_blank">Dr. Guillaume Sartoretti</a>. Before that, I got my bachelor degree in Beihang Univeristy in China.</p>
248
249<p>My research began with deep reinforcement learning-based navigation and is evolving toward unifying high-level decision-making with advanced mobility. I aim to develop generalizable learning frameworks with solid systems engineering, to enable intelligent and reliable robot operations in the real world.</p>
250
251<p>In my spare time, I am a casual athlete in basketball and swimming. During my undergraduate, I was the captain of the department basketball team and the coach of the university swimming assiociation (However I found I spend less time on sports since I started my PhD <img class="emoji" title=":stuck_out_tongue_closed_eyes:" alt=":stuck_out_tongue_closed_eyes:" src="https://github.githubassets.com/images/icons/emoji/unicode/1f61d.png" height="20" width="20">).</p>
252
253<p style="color: red; font-weight: bold;">
254⚠️ My NUS staff email ([email protected]) is no longer active. 
255Please contact me via my alumni email: [email protected].
256</p>
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264 
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295      <table class="table table-sm table-borderless">
296        
297        
298        
299          <tr>
300            <th scope="row" style="width: 20%">Jun 15, 2026</th>
301            <td>
302              
303                Three of my co-author works on robust perceptive locomotion, efficient locomotion learning via action prior, and embodied navigation through imagination have been accepted to <a href="https://2026.ieee-iros.org/" rel="external nofollow noopener" target="_blank">IROS 2026</a>!
304
305              
306            </td>
307          </tr>
308        
309          <tr>
310            <th scope="row" style="width: 20%">May 06, 2026</th>
311            <td>
312              
313                My co-author work which tackles multi-agent cooperative navigation by learning role play through option critic has been accepted by <a href="https://www.ieee-ras.org/publications/ra-l" rel="external nofollow noopener" target="_blank">RA-L</a>!
314
315              
316            </td>
317          </tr>
318        
319          <tr>
320            <th scope="row" style="width: 20%">May 02, 2026</th>
321            <td>
322              
323                After six incredible years, today is my last day at NUS. This place shaped me into a PhD, a postdoc, and, more importantly, a better person. I am truly grateful to all the professors, mentors, colleagues, and friends who made this journey so meaningful!
324
325              
326            </td>
327          </tr>
328        
329          <tr>
330            <th scope="row" style="width: 20%">Jan 31, 2026</th>
331            <td>
332              
333                My co-author work which improves our attention-based exploration pipeline through graph transformer and Kalman Filter has been accepted by <a href="https://2026.ieee-icra.org/" rel="external nofollow noopener" target="_blank">ICRA2026</a>!
334
335              
336            </td>
337          </tr>
338        
339          <tr>
340            <th scope="row" style="width: 20%">Dec 02, 2025</th>
341            <td>
342              
343                I had the pleasure of giving an online talk on learning-based autonomous robot exploration, invited by Prof. <a href="https://mee.sustech.edu.cn/jiaozhiyuangong/3552.html" rel="external nofollow noopener" target="_blank">Boyu Zhou</a> from SUSTech.
344
345              
346            </td>
347          </tr>
348        
349          <tr>
350            <th scope="row" style="width: 20%">Oct 20, 2025</th>
351            <td>
352              
353                Our work on scalable autonomous robot exploration based on expert guided reinforcement learning received the best paper award at the <a href="https://activep-ws.github.io/index.html" rel="external nofollow noopener" target="_blank">IROS2025 active perception workshop</a>!
354
355              
356            </td>
357          </tr>
358        
359          <tr>
360            <th scope="row" style="width: 20%">Oct 14, 2025</th>
361            <td>
362              
363                I am honored to be a speaker at the <a href="https://macrai2025.github.io/" rel="external nofollow noopener" target="_blank">Multi-Agent Cooperative Systems and Swarm Robotics in the Era of Generative AI</a> workshop at <a href="https://www.iros25.org/" rel="external nofollow noopener" target="_blank">IROS2025</a>.
364
365              
366            </td>
367          </tr>
368        
369          <tr>
370            <th scope="row" style="width: 20%">Aug 02, 2025</th>
371            <td>
372              
373                Three of my co-authored papers have been accepted to <a href="https://www.corl.org/" rel="external nofollow noopener" target="_blank">CORL2025</a>! Covering map prediction for learning-based robot exploration, test-time adaptation for vision-language-based outdoor animal search, and a novel approach to the general Traveling Salesman Problem.
374
375              
376            </td>
377          </tr>
378        
379          <tr>
380            <th scope="row" style="width: 20%">Apr 11, 2025</th>
381            <td>
382              
383                My co-author work on bio-inspired torque control for locomotion is accepted by <a href="https://roboticsconference.org/" rel="external nofollow noopener" target="_blank">RSS</a>!
384
385              
386            </td>
387          </tr>
388        
389          <tr>
390            <th scope="row" style="width: 20%">Apr 01, 2025</th>
391            <td>
392              
393                My co-author work on LLM-based multi-agent path-finding is accepted by <a href="https://2025.ijcnn.org/" rel="external nofollow noopener" target="_blank">IJCNN</a>!
394
395              
396            </td>
397          </tr>
398        
399          <tr>
400            <th scope="row" style="width: 20%">Jan 28, 2025</th>
401            <td>
402              
403                Our paper on diffusion-based robot exploration is accepted by <a href="https://2025.ieee-icra.org/" rel="external nofollow noopener" target="_blank">ICRA2025</a>, along with two of my co-author works on multi-robot exploration with limited field-of-view and consensus for multi-robot path-finding inspired by sheaf theory!
404
405              
406            </td>
407          </tr>
408        
409          <tr>
410            <th scope="row" style="width: 20%">Jan 10, 2025</th>
411            <td>
412              
413                My co-author work on learning-based heterogenous task allocation is accepted by <a href="https://www.ieee-ras.org/publications/ra-l" rel="external nofollow noopener" target="_blank">RA-L</a>!
414
415              
416            </td>
417          </tr>
418        
419          <tr>
420            <th scope="row" style="width: 20%">
420Nov 06, 2024</th>
421            <td>
422              
423                My co-author work on hierarchical decision networks for robot exploration and navigation is accepted by <a href="https://www.ieee-ras.org/publications/ra-l" rel="external nofollow noopener" target="_blank">RA-L</a>!
424
425              
426            </td>
427          </tr>
428        
429          <tr>
430            <th scope="row" style="width: 20%">Sep 05, 2024</th>
431            <td>
432              
433                My co-author work on multi-robot adversarial search is accepted by <a href="https://www.corl.org/" rel="external nofollow noopener" target="_blank">CORL2024</a>!
434
435              
436            </td>
437          </tr>
438        
439          <tr>
440            <th scope="row" style="width: 20%">Aug 08, 2024</th>
441            <td>
442              
443                My co-author work on low-cost multi-UAV system development is accepted by <a href="https://2024.iccas.org/" rel="external nofollow noopener" target="_blank">ICCAS2024</a>!
444
445              
446            </td>
447          </tr>
448        
449          <tr>
450            <th scope="row" style="width: 20%">Jul 17, 2024</th>
451            <td>
452              
453                My co-author work on multi-agent flocking is accepted as extended abstract by <a href="https://icra40.ieee.org/" rel="external nofollow noopener" target="_blank">ICRA@40</a>!
454
455              
456            </td>
457          </tr>
458        
459          <tr>
460            <th scope="row" style="width: 20%">Jul 02, 2024</th>
461            <td>
462              
463                My co-author paper on multi-robot exploration under bandwidth constratint has been accepted by <a href="https://dars2024.engineering.cornell.edu/" rel="external nofollow noopener" target="_blank">DARS2024</a>!
464
465              
466            </td>
467          </tr>
468        
469          <tr>
470            <th scope="row" style="width: 20%">Jun 30, 2024</th>
471            <td>
472              
473                My co-author paper on multi-robot exploration under limited communication range has been accepted by <a href="https://iros2024-abudhabi.org/" rel="external nofollow noopener" target="_blank">IROS2024</a>!
474
475              
476            </td>
477          </tr>
478        
479          <tr>
480            <th scope="row" style="width: 20%">Mar 08, 2024</th>
481            <td>
482              
483                A new beginning! I am now a postdoc fellow at NUS ME.
484
485              
486            </td>
487          </tr>
488        
489          <tr>
490            <th scope="row" style="width: 20%">Feb 29, 2024</th>
491            <td>
492              
493                Our paper on larg-scale robot exploration has been accepted by <a href="https://www.ieee-ras.org/publications/ra-l" rel="external nofollow noopener" target="_blank">RA-L</a>!
494
495              
496            </td>
497          </tr>
498        
499          <tr>
500            <th scope="row" style="width: 20%">Feb 28, 2024</th>
501            <td>
502              
503                I obtained my PhD degree from NUS. Thanks to all who support me along this wonderful journey!
504
505              
506            </td>
507          </tr>
508        
509          <tr>
510            <th scope="row" style="width: 20%">Jan 15, 2024</th>
511            <td>
512              
513                I passed the oral defense for my PhD thesis. A lot of thanks to my supervisor <a href="https://www.marmotlab.org/bio.html" rel="external nofollow noopener" target="_blank">Prof. Guillaume Sartoretti</a> and my examiners: <a href="https://cde.nus.edu.sg/me/staff/ang-jr-marcelo-h/" rel="external nofollow noopener" target="_blank">Prof. Marcelo Ang</a> and <a href="https://cde.nus.edu.sg/me/staff/chen-chao-yu-peter/" rel="external nofollow noopener" target="_blank">Prof. Chen Chao Yu Peter</a>!
514
515              
516            </td>
517          </tr>
518        
519          <tr>
520            <th scope="row" style="width: 20%">Sep 22, 2023</th>
521            <td>
522              
523                My co-author paper on multi-agent informative path planning is accepted by <a href="https://sites.bu.edu/mrs2023/" rel="external nofollow noopener" target="_blank">MRS2023</a>!
524
525              
526            </td>
527          </tr>
528        
529          <tr>
530            <th scope="row" style="width: 20%">Aug 31, 2023</th>
531            <td>
532              
533                My co-author paper on navigation in unknown environments is accepted by <a href="https://www.corl2023.org/" rel="external nofollow noopener" target="_blank">CoRL2023</a>!
534
535              
536            </td>
537          </tr>
538        
539          <tr>
540            <th scope="row" style="width: 20%">Jun 22, 2023</th>
541            <td>
542              
543                My co-author paper on persistent monitoring is accepted by <a href="https://ieee-iros.org/" rel="external nofollow noopener" target="_blank">IROS2023</a>!
544
545              
546            </td>
547          </tr>
548        
549          <tr>
550            <th scope="row" style="width: 20%">Jan 17, 2023</th>
551            <td>
552              
553                Our paper on autonomous robot exploration is accepted by <a href="https://www.icra2023.org/" rel="external nofollow noopener" target="_blank">ICRA2023</a>!
554
555              
556            </td>
557          </tr>
558        
559          <tr>
560            <th scope="row" style="width: 20%">Nov 21, 2022</th>
561            <td>
562              
563                Our team wins a golden prize in <a href="https://cy.ncss.cn/en/" rel="external nofollow noopener" target="_blank">The 8th China International College Students’ “Internet+” Innovation and Entrepreneurship Competition</a>! Our project stands out as the final 200 golden-prize projects from more than one million teams.
564
565              
566            </td>
567          </tr>
568        
569          <tr>
570            <th scope="row" style="width: 20%">Sep 10, 2022</th>
571            <td>
572              
573                Two papers accepted on the same day! My first paper on multiple traveling salesmen problem is accepted by <a href="https://dars2022.org/#/" rel="external nofollow noopener" target="_blank">DARS2022</a>. Another paper on informative path planning is accepted by <a href="https://corl2022.org/" rel="external nofollow noopener" target="_blank">CoRL2022</a>.
574
575              
576            </td>
577          </tr>
578        
579          <tr>
580            <th scope="row" style="width: 20%">Feb 28, 2022</th>
581            <td>
582              
583                I transfered from master of engineering to dotor of philosophy! Sadly I can not be a “master” anymore <img class="emoji" title=":smile:" alt=":smile:" src="https://github.githubassets.com/images/icons/emoji/unicode/1f604.png" height="20" width="20">.
584
585              
586            </td>
587          </tr>
588        
589          <tr>
590            <th scope="row" style="width: 20%">Aug 03, 2020</th>
591            <td>
592              
593                I started my master of engineering in <a href="https://cde.nus.edu.sg/me/" rel="external nofollow noopener" target="_blank">Department of Mechanical Engineering, National University of Singapore</a>, under the supervision of <a href="https://www.marmotlab.org/" rel="external nofollow noopener" target="_blank">Dr. Guillaume Sartoretti</a>.
594
595              
596            </td>
597          </tr>
598        
599          <tr>
600            <th scope="row" style="width: 20%">Jun 30, 2020</th>
601            <td>
602              
603                I received my bachelor degree in mechanical engineering from <a href="http://www.me.buaa.edu.cn/" rel="external nofollow noopener" target="_blank">
603School of Mechanical Engineering and Automation, Beihang University</a>. I am also awarded as the outstanding graduate of Beihang University!
604
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606            </td>
607          </tr>
608        
609          <tr>
610            <th scope="row" style="width: 20%">Sep 01, 2016</th>
611            <td>
612              
613                I started my bachelor in mechanical engineering at <a href="http://www.me.buaa.edu.cn/" rel="external nofollow noopener" target="_blank">School of Mechanical Engineering and Automation, Beihang University</a>.
614
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621  
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623
624    
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626    <!-- Latest posts -->
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628
629    <!-- Selected papers -->
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632        <a href="/publications/" style="color: inherit">Selected Publications</a>
633      </h2>
634      <div class="publications">
635  <ol class="bibliography">
636<li>
637<div class="row">
638  
639    <div class="col col-sm-2 abbr">
640      
641        
642          
643          
644
645<figure>
646  <picture>
647    <!-- Auto scaling with imagemagick -->
648    <!--
649      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
650      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
651    -->
652    
653      <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">
654    
655    <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();">
656  </source></picture>
657
658  
659</figure>
660
661        
662      
663    </div>
664  
665
666  <!-- Entry bib key -->
667  <div id="cao2025header" class="col-sm-8">
668    <!-- Title -->
669    <div class="title">HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward</div>
670    <!-- Author -->
671    <div class="author">
672      
673
674      
675      <em>Yuhong
676            Cao</em>, Yizhuo
677            Wang, Jingsong
678            Liang, Shuhao
679            Liao, Yifeng
680            Zhang, Peizhuo
681            Li, and Guillaume
682            Sartoretti
683    </div>
684
685    <!-- Journal/Book title and date -->
686    
687    
688    
689    
690    
691    
692    
693    
694    
695    
696    <div class="periodical">
697      <em>Under review</em>,  2025
698    </div>
699    <div class="periodical">
700      
701    </div>
702
703    <!-- Links/Buttons -->
704    <div class="links">
705      
706      
707        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
708      
709      
710      
711        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
712      
713      
714      
715        
716          <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>
717        
718      
719      
720      
721        <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>
722      
723      
724      
725        <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>
726      
727      
728      
729      
730    </div>
731    
732      
733      
734      
735      
736    
737
738    
739
740    
741      <!-- Hidden abstract block -->
742      <div class="abstract hidden">
743        <p>This work pushes the boundaries of learning-based methods in autonomous robot exploration in terms of environmental scale and exploration efficiency. 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 bi
743as 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>
744      </div>
745    
746
747    
748      <!-- Hidden bibtex block -->
749      <div class="bibtex hidden">
750        <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>
751  <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>
752  <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>
753  <span class="na">journal</span> <span class="p">=</span> <span class="s">{Under review}</span><span class="p">,</span>
754  <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span>
755<span class="p">}</span></code></pre></figure>
756      </div>
757    
758
759    
760  </div>
761</div>
762</li>
763<li>
764<div class="row">
765  
766    <div class="col col-sm-2 abbr">
767      
768        
769          
770          
771
772<figure>
773  <picture>
774    <!-- Auto scaling with imagemagick -->
775    <!--
776      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
777      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
778    -->
779    
780      <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">
781    
782    <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();">
783  </source></picture>
784
785  
786</figure>
787
788        
789      
790    </div>
791  
792
793  <!-- Entry bib key -->
794  <div id="cao2025dare" class="col-sm-8">
795    <!-- Title -->
796    <div class="title">DARE: Diffusion Policy for Autonomous Robot Exploration</div>
797    <!-- Author -->
798    <div class="author">
799      
800
801      
802      <em>Yuhong
803            Cao</em>, Jeric
804            Lew, Jingsong
805            Liang, Jin
806            Cheng, and Guillaume
807            Sartoretti
808    </div>
809
810    <!-- Journal/Book title and date -->
811    
812    
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818    
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820    
821    <div class="periodical">
822      <em>2025 IEEE International Conference on Robotics and Automation (ICRA)</em>,  2025
823    </div>
824    <div class="periodical">
825      
826    </div>
827
828    <!-- Links/Buttons -->
829    <div class="links">
830      
831      
832        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
833      
834      
835      
836        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
837      
838      
839      
840        
841          <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>
842        
843      
844      
845      
846        <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>
847      
848      
849      
850        <a href="https://github.com/marmotlab/DARE" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
851      
852      
853      
854      
855    </div>
856    
857      
858      
859      
860      
861    
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864
865    
866      <!-- Hidden abstract block -->
867      <div class="abstract hidden">
868        <p>
868Autonomous 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>
869      </div>
870    
871
872    
873      <!-- Hidden bibtex block -->
874      <div class="bibtex hidden">
875        <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>
876  <span class="na">title</span> <span class="p">=</span> <span class="s">{DARE: Diffusion Policy for Autonomous Robot Exploration}</span><span class="p">,</span>
877  <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>
878  <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>
879  <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span>
880  <span class="na">publisher</span> <span class="p">=</span> <span class="s">{IEEE}</span>
881<span class="p">}</span></code></pre></figure>
882      </div>
883    
884
885    
886  </div>
887</div>
888</li>
889<li>
890<div class="row">
891  
892    <div class="col col-sm-2 abbr">
893      
894        
895          
896          
897
898<figure>
899  <picture>
900    <!-- Auto scaling with imagemagick -->
901    <!--
902      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
903      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
904    -->
905    
906      <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">
907    
908    <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();">
909  </source></picture>
910
911  
912</figure>
913
914        
915      
916    </div>
917  
918
919  <!-- Entry bib key -->
920  <div id="cao2024deep" class="col-sm-8">
921    <!-- Title -->
922    <div class="title">Deep Reinforcement Learning-based Large-scale Robot Exploration</div>
923    <!-- Author -->
924    <div class="author">
925      
926
927      
928      <em>Yuhong
929            Cao</em>, Rui
930            Zhao, Yizhuo
931            Wang, Bairan
932            Xiang, and Guillaume
933            Sartoretti
934    </div>
935
936    <!-- Journal/Book title and date -->
937    
938    
939    
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941    
942    
943    
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946    
947    <div class="periodical">
948      <em>IEEE Robotics and Automation Letters</em>,  2024
949    </div>
950    <div class="periodical">
951      
952    </div>
953
954    <!-- Links/Buttons -->
955    <div class="links">
956      
957      
958        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
959      
960      
961      
962        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
963      
964      
965        
966          <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>
967        
968      
969      
970        
971          <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>
972        
973      
974      
975      
976        <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>
977      
978      
979      
980        <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>
981      
982      
983      
984      
985    </div>
986    
987      
988      
989      
990      
991    
992
993    
994
995    
996      <!-- Hidden abstract block -->
997      <div class="abstract hidden">
998        <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
998ncy (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>
999      </div>
1000    
1001
1002    
1003      <!-- Hidden bibtex block -->
1004      <div class="bibtex hidden">
1005        <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>
1006  <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>
1007  <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>
1008  <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Robotics and Automation Letters}</span><span class="p">,</span>
1009  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
1010  <span class="na">publisher</span> <span class="p">=</span> <span class="s">{IEEE}</span>
1011<span class="p">}</span></code></pre></figure>
1012      </div>
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1015    
1016  </div>
1017</div>
1018</li>
1019<li>
1020<div class="row">
1021  
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1023      
1024        
1025          
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1028<figure>
1029  <picture>
1030    <!-- Auto scaling with imagemagick -->
1031    <!--
1032      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
1033      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
1034    -->
1035    
1036      <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">
1037    
1038    <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();">
1039  </source></picture>
1040
1041  
1042</figure>
1043
1044        
1045      
1046    </div>
1047  
1048
1049  <!-- Entry bib key -->
1050  <div id="cao2023ariadne" class="col-sm-8">
1051    <!-- Title -->
1052    <div class="title">Ariadne: A reinforcement learning approach using attention-based deep networks for exploration</div>
1053    <!-- Author -->
1054    <div class="author">
1055      
1056
1057      
1058      <em>Yuhong
1059            Cao</em>, Tianxiang
1060            Hou, Yizhuo
1061            Wang, Xian
1062            Yi, and Guillaume
1063            Sartoretti
1064    </div>
1065
1066    <!-- Journal/Book title and date -->
1067    
1068    
1069    
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1071    
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1073    
1074    
1075    
1076    
1077    <div class="periodical">
1078      <em>In 2023 IEEE International Conference on Robotics and Automation (ICRA)</em> ,  2023
1079    </div>
1080    <div class="periodical">
1081      
1082    </div>
1083
1084    <!-- Links/Buttons -->
1085    <div class="links">
1086      
1087      
1088        <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
1089      
1090      
1091      
1092        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1093      
1094      
1095        
1096          <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>
1097        
1098      
1099      
1100        
1101          <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>
1102        
1103      
1104      
1105      
1106        <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>
1107      
1108      
1109      
1110        <a href="https://github.com/marmotlab/ARiADNE" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
1111      
1112      
1113      
1114      
1115    </div>
1116    
1117      
1118      
1119      
1120      
1121    
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1123    
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1125    
1126      <!-- Hidden abstract block -->
1127      <div class="abstract hidden">
1128        <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>
1129      </div>
1130    
1131
1132    
1133      <!-- Hidden bibtex block -->
1134      <div class="bibtex hidden">
1135        <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>
1136  <span class="na">title</span> <span class="p">=</span> <span class="s">
1136{Ariadne: A reinforcement learning approach using attention-based deep networks for exploration}</span><span class="p">,</span>
1137  <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>
1138  <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>
1139  <span class="na">pages</span> <span class="p">=</span> <span class="s">{10219--10225}</span><span class="p">,</span>
1140  <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span>
1141  <span class="na">organization</span> <span class="p">=</span> <span class="s">{IEEE}</span>
1142<span class="p">}</span></code></pre></figure>
1143      </div>
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1149</li>
1150<li>
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1154      
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1156          
1157          
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1159<figure>
1160  <picture>
1161    <!-- Auto scaling with imagemagick -->
1162    <!--
1163      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
1164      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
1165    -->
1166    
1167      <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">
1168    
1169    <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();">
1170  </source></picture>
1171
1172  
1173</figure>
1174
1175        
1176      
1177    </div>
1178  
1179
1180  <!-- Entry bib key -->
1181  <div id="cao2023catnipp" class="col-sm-8">
1182    <!-- Title -->
1183    <div class="title">CAtNIPP: Context-aware attention-based network for informative path planning</div>
1184    <!-- Author -->
1185    <div class="author">
1186      
1187
1188      
1189      <em>Yuhong
1190            Cao</em>, Yizhuo
1191            Wang, Apoorva
1192            Vashisth, Haolin
1193            Fan, and Guillaume Adrien
1194            Sartoretti
1195    </div>
1196
1197    <!-- Journal/Book title and date -->
1198    
1199    
1200    
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1208    <div class="periodical">
1209      <em>In Conference on Robot Learning</em> ,  2022
1210    </div>
1211    <div class="periodical">
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1228        
1229      
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1232          <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>
1233        
1234      
1235      
1236      
1237        <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>
1238      
1239      
1240      
1241        <a href="https://github.com/marmotlab/CAtNIPP" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
1242      
1243      
1244      
1245      
1246    </div>
1247    
1248      
1249      
1250      
1251      
1252    
1253
1254    
1255
1256    
1257      <!-- Hidden abstract block -->
1258      <div class="abstract hidden">
1259        <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 
1259self-attention for its powerful ability to capture 
1260dependencies 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 
1261that 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>
1262      </div>
1263    
1264
1265    
1266      <!-- Hidden bibtex block -->
1267      <div class="bibtex hidden">
1268        <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>
1269  <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>
1270  <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>
1271  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Conference on Robot Learning}</span><span class="p">,</span>
1272  <span class="na">pages</span> <span class="p">=</span> <span class="s">{1928--1937}</span><span class="p">,</span>
1273  <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span class="p">,</span>
1274  <span class="na">organization</span> <span class="p">=</span> <span class="s">{PMLR}</span>
1275<span class="p">}</span></code></pre></figure>
1276      </div>
1277    
1278
1279    
1280  </div>
1281</div>
1282</li>
1283<li>
1284<div class="row">
1285  
1286    <div class="col col-sm-2 abbr">
1287      
1288        
1289          
1290          
1291
1292<figure>
1293  <picture>
1294    <!-- Auto scaling with imagemagick -->
1295    <!--
1296      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
1297      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
1298    -->
1299    
1300      <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">
1301    
1302    <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();">
1303  </source></picture>
1304
1305  
1306</figure>
1307
1308        
1309      
1310    </div>
1311  
1312
1313  <!-- Entry bib key -->
1314  <div id="cao2022dan" class="col-sm-8">
1315    <!-- Title -->
1316    <div class="title">Dan: Decentralized attention-based neural network for the minmax multiple traveling salesman problem</div>
1317    <!-- Author -->
1318    <div class="author">
1319      
1320
1321      
1322      <em>Yuhong
1323            Cao</em>, Zhanhong
1324            Sun, and Guillaume
1325            Sartoretti
1326    </div>
1327
1328    <!-- Journal/Book title and date -->
1329    
1330    
1331    
1332    
1333    
1334    
1335    
1336    
1337    
1338    
1339    <div class="periodical">
1340      <em>In International Symposium on Distributed Autonomous Robotic Systems</em> ,  2022
1341    </div>
1342    <div class="periodical">
1343      
1344    </div>
1345
1346    <!-- Links/Buttons -->
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1348      
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1353      
1354      
1355      
1356        <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
1357      
1358      
1359        
1360          <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>
1361        
1362      
1363      
1364        
1365          <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>
1366        
1367      
1368      
1369      
1370        <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>
1371      
1372      
1373      
1374        <a href="https://github.com/marmotlab/DAN" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
1375      
1376      
1377      
1378      
1379    </div>
1380    
1381      
1382      
1383      
1384      
1385    
1386
1387    
1388      <!-- Hidden Award block -->
1389      <div class="award hidden d-print-inline">
1390        <p></p>
1391<p>Best student paper</p>
1392
1393      </div>
1394    
1395
1396    
1397      <!-- Hidden abstract block -->
1398      <div class="abstract hidden">
1399        <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 
1399small- 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>
1400      </div>
1401    
1402
1403    
1404      <!-- Hidden bibtex block -->
1405      <div class="bibtex hidden">
1406        <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>
1407  <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>
1408  <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>
1409  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{International Symposium on Distributed Autonomous Robotic Systems}</span><span class="p">,</span>
1410  <span class="na">pages</span> <span class="p">=</span> <span class="s">{202--215}</span><span class="p">,</span>
1411  <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span class="p">,</span>
1412  <span class="na">organization</span> <span class="p">=</span> <span class="s">{Springer}</span>
1413<span class="p">}</span></code></pre></figure>
1414      </div>
1415    
1416
1417    
1418  </div>
1419</div>
1420</li>
1421</ol>
1422</div>
1423
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1428    
1429    <div class="picture">
1430    
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1432<figure>
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1436      See https://www.debugbear.com/blog/responsive-images#w-descriptors-and-the-sizes-attribute and
1437      https://developer.mozilla.org/en-US/docs/Learn/HTML/Multimedia_and_embedding/Responsive_images for info on defining 'sizes' for responsive images
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1441    
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1447
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1449        <a style="align-items: center; display: flex; justify-content: center;">This is my hometown Chongqing. It is situated at the confluence of the Yangtze River and the Jialing River.</a>
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1575     * Couple of changes were made to the original code to make it compatible
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1583     * the desired size. This mistake, however, breaks the computations as the
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