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workshop on learned robot representations rss 2025 home call for papers dates schedule committees workshop on learned robot representations roboreps rss 2025 los angeles ca wednesday june 25th 2025 room sgm 124 email rss25 roboreps gmail com the workshop is now over thank you to all of our speakers panelists presenters and attendees for allowing us to run such a wonderful and productive workshop we hope that the discussions fostered by it gave valuable insights into robot representation learning overview general purpose robotic systems require powerful representations and abstractions in deployment such robots are expected to encounter diverse and complex scenarios while recent large scale learned models exhibit remarkable generalization similarly getting representations that can flexibly generalize to all the unanticipated situations a robot might face remains challenging especially given the cost of robot data thus it is important to investigate how to best learn generalizable representations evaluate their effectiveness and leverage them for downstream robotics tasks ideally these representations should capture 1 spatial dynamic information needed for fine grained control 2 semantic information required for common sense reasoning and scene understanding and 3 knowledge of conventions needed for smooth human robot interactions additionally these representations must be robust to the diversity of tasks scenes and operators the robot will encounter in this workshop we aim to explore the following what makes a good robot representation how can we learn them and how can we most effectively make use of them our speakers and panelists are pioneering robotics and machine learning researchers defining the state of the art on a range of topics including end to end control task and motion planning tamp human robot interaction hri scene understanding slam and more we invite the community for submissions in these areas as well as from a wider set of perspectives for example submissions addressing how the following fields might guide robotics research 1 deep representation learning in vision and language 2 learning representations for field robotics and ai where data is extremely scarce or noisy or 3 bias and robustness in neural representations areas of interest we aim to investigate the following topics and research questions what sorts of pre training tasks objectives data or models yield good representations for robotics how do we evaluate or choose good pre trained models to fine tune into robot policies or leverage in a perception or control stack is embodied or grounded data necessary for good robot representations or is general pre training data sufficient when is fine tuning large models from other domains enough for robotic foundation models and when do we need robot specific large scale training how do we elicit representations at a level of granularity and semantic abstraction that is appropriate for robot tasks what functionalities do pre trained representations grant robots e g common sense semantic understanding what to look for reasoning about semantics dynamics spatial features or motion how to act conventions for interacting with humans dialogue goal specification information gathering interpretability how are these pre trained representations best integrated into robot systems e g end to end learned policies learned with bc or rl e g rt 2 openvla zero shot controllers e g code as policies moka hand crafted scene or task representations e g semantic scene graphs lerfs neural tamp image generation as subgoals how can learned representations be de biased such that the robots using them can robustly operate in underrepresented environments e g households from cultures around the world not just conventional households understand language and behaviors from diverse human operators we also give a non exhaustive list of keywords foundation models for zero shot planning control reasoning scene understanding slam and hri representation learning for policy learning vision language action models vlas reinforcement learning cross embodiment transfer representation learning for perception and scene understanding nerf lerf gaussian splatting inverse graphics reconstruction metric semantic scene graphs representation learning for planning neural tamp learned dynamics world modeling chain of thought reasoning generated image subgoals data for embodied grounded or spatial reasoning submission guidelines submission portal now closed openreview we are accepting workshop submissions of the following types papers up to 6 pages plus unlimited references appendices extended abstracts up to 2 pages plus unlimited references appendices we request that submissions are in the rss format they should not be anonymized additionally you may submit papers that are under review at other venues or submitted to other workshops best paper learning long context diffusion policies via past token prediction important dates paper submission deadline may 28 2025 23 59 aoe paper acceptance june 11 2025 camera ready version due june 16 2025 23 59 aoe workshop june 25 2025 schedule session 1 8 40 am 8 50 am opening remarks 8 50 am 9 30 am invited talk 1 wolfram burgard virtual 9 30 am 10 20 am poster session a coffee break session 2 10 20 am 11 55 am invited talks 2 3 4 liam paull chelsea finn mahi shafiullah 11 55 am 12 30 pm panel 12 30 pm 2 00 pm lunch break session 3 2 00 pm 2 20 pm spotlight talks 2 20 pm 3 00 pm invited talk 5 krishna murthy 3 00 pm 4 00 pm poster session b coffee break session 4 4 00 pm 4 40 pm invited talk 6 andreea bobu 4 40 pm 5 00 pm closing remarks papers and poster session assignments asterisk after paper title indicates spotlight talk poster session a 9 30 am 10 20 am top erl transformer based off policy episodic reinforcement learning ge li dong tian hongyi zhou xinkai jiang rudolf lioutikov gerhard neumann enter the mind palace reasoning and planning for long term active embodied question answering muhammad fadhil ginting dong ki kim xiangyun meng andrzej marek reinke jai krishna bandi navid kayhani oriana peltzer david fan amirreza shaban sung kyun kim mykel kochenderfer ali akbar agha mohammadi shayegan omidshafiei learning attentive neural processes for planning with pushing actions atharv jain seiji a shaw nicholas roy interpretable human in the loop in context preference learning via preference boundaries valerie k chen julie shah andreea bobu online latent factor representation learning alejandro murillo gonzález lantao liu dexwild dexterous human interactions for in the wild robot policies tony tao mohan kumar srirama jason jingzhou liu kenneth shaw deepak pathak grim task oriented grasping with conditioning on generative examples shailesh alok raj nayan kumar priya shukla andrew melnik michael beetz gora chand nandi bi manual joint camera calibration and scene representation haozhan tang tianyi zhang matthew johnson roberson william zhi disdp robust imitation learning via disentangled diffusion policies pankhuri vanjani paul mattes kevin daniel kuryshev xiaogang jia vedant dave rudolf lioutikov rayfronts open set semantic ray frontiers for online scene understanding and exploration omar alama avigyan bhattacharya haoyang he seungchan kim yuheng qiu wenshan wang cherie ho nikhil varma keetha sebastian scherer learning factorized diffusion policies for conditional action diffusion omkar patil prabin kumar rath kartikay milind pangaonkar eric rosen nakul gopalan learning symbolic world model representations for long horizon robot planning naman shah jayesh nagpal siddharth srivastava womap world models for embodied open vocabulary object localization tenny yin zhiting mei tao sun lihan zha emily zhou jeremy bao miyu yamane ola sho anirudha majumdar rewind language guided rewards teach robot policies without new demonstrations jiahui zhang yusen luo abrar anwar sumedh anand sontakke joseph j lim jesse thomason erdem biyik jesse zhang poster session b 3 00 pm 4 00 pm dream differentiable real to sim to real engine for learning robotic manipulation haozhe lou mingtong zhang haoran geng hanyang zhou sicheng he zhiyuan gao siheng zhao jiageng mao pieter abbeel jitendra malik daniel seita yue wang learning long context diffusion policies via past token prediction marcel torne andy tang yuejiang liu chelsea finn h 3 dp triply hierarchical diffusion policy for visuomotor learning yiyang lu yufeng tian zhecheng yuan xianbang wang pu hua zhengrong xue huazhe xu robo2vlm visual question answering from large scale in the wild robot manipulation datasets kaiyuan chen shuangyu xie zehan ma pannag r sanketi ken goldberg implicit contact representations with neural descriptor fields for learning dynamic recovery policies fan yang sergio francisco aguilera marinovic soshi iba rana soltani zarrin dmitry berenson cl hcotnav closed loop hierarchical chain of thought for zero shot object goal navigation with vision language models yuxin cai haoruo zhang wei yun yau chen lv xpg rl reinforcement learning with explainable priority guidance for efficiency boosted mechanical search yiting zhang shichen li elena shrestha importance weighted retrieval for few shot imitation learning amber xie rahul chand dorsa sadigh joey hejna point policy unifying observations and actions with key points for robot manipulation siddhant haldar lerrel pinto a steerable vision language action framework for autonomous driving tian gao catherine glossop kyle stachowicz timothy gao celine tan oier mees yuejiang liu sergey levine dorsa sadigh chelsea finn graphseg segmented 3d representations via graph edge addition and contraction haozhan tang tianyi zhang oliver kroemer matthew johnson roberson william zhi seeing the bigger picture 3d latent mapping for mobile manipulation policy learning sunghwan kim woojeh chung yulun tian zhirui dai arth shukla hao su nikolay atanasov skillwrapper autonomously learning interpretable skill abstractions with foundation models ziyi yang benned hedegaard ahmed jaafar skye thompson yichen wei everest yang haotian fu shreyas sundara raman stefanie tellex george konidaris david paulius naman shah structured 3d scene queries with graph databases aaron ray luca carlone egozero robot learning from smart glasses vincent liu ademi adeniji haotian zhan siddhant haldar raunaq bhirangi pieter abbeel lerrel pinto feel the force contact driven learning from humans ademi adeniji zhuoran chen vincent liu venkatesh pattabiraman siddhant haldar raunaq bhirangi pieter abbeel lerrel pinto beast efficient tokenization of b splines encoded action sequences for imitation learning hongyi zhou weiran liao xi huang yucheng tang fabian otto xiaogang jia xinkai jiang simon hilber ge li qian wang ömer erdinç yağmurlu nils blank moritz reuss rudolf lioutikov invited speakers mahi shafiullah new york university usa andreea bobu massachusetts institute of technology usa chelsea finn stanford university physical intelligence usa wolfram burgard university of technology nuremberg germany liam paull university of montreal canada krishna murthy jatavallabhula meta usa organizing committees william chen u c berkeley dominic maggio massachusetts institute of technology mara levy university of maryland dhruv shah google deepmind jared strader massachusetts institute of technology kuan fang cornell university the template for this website is borrowed from the corl 2024 leap workshop all credit goes to the creators of said website