Robot Imitation Learning

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172 papers in the last four weeks, up 310% on the four weeks before. 1.0% of all new papers.

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LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rollouts
Oct 13, 2026Wenxuan Guo, Yingping Liang, Zhaoguo Wang +11Robot Policy Learning Robot Policy Adaptation
Tsinghua University · Dexmal · Loopeva
Contact Trajectory Prompting: In-Context Transfer of Contact-Rich Behaviors from a Single Demonstration
Oct 13, 2026Xian Nie, Yujie Zang, Yuhang Zheng +8Contact-Rich Robotic Manipulation Robot Imitation Learning
Shanghai Jiao Tong University · TARS Robotics · National University of Singapore +2
Steertuning Policies from a Few Human Videos
Oct 13, 2026Billy Yan, Isaac Tu, Kelly Lee +8Robot Policy Adaptation Data-Efficient Robot Learning
New York University
OneDemo: Unified Policy Learning and Data Generation from a Single Demonstration
Oct 13, 2026Ying Qian, Jianshu Hu, Han Fang +2Robot Policy Learning Learning from Demonstration
Stanford University · Shanghai Jiao Tong University · Tsinghua University
RESTA: Time-Scaling Augmentation for Data-Efficient Robot Imitation Learning
Oct 13, 2026Yunpeng Jiang, Jianshu Hu, Han Fang +3Data-Efficient Robot Learning Robot Imitation Learning
Global College, Shanghai Jiao Tong University, Shanghai, China · Shanghai Institute of Physical Intelligence and Robotics (PAIR), Shanghai, China · Duke Kunshan University, Jiangsu, China
Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations
Oct 13, 2026Bo-Ruei Huang, Daniel Seita, Erdem BıyıkRobotic Manipulation Contact-Rich Robotic Manipulation
Thomas Lord Department of Computer Science, University of Southern California
DexStitch: Learning and Composing Dexterous Skills from Human Videos via Contact-Aware Robotic Inpainting
Oct 13, 2026Byeonggyeol Choi, Seongmin Jung, Unghui Nam +5Robot Skill Learning Long-Horizon Robotic Manipulation
Seoul National University · Robotics Lab, Hyundai Motor Company · DGIST
Learning Robot Policies for Whole-Body Mobility Assistance via Dynamic Human Motion Generation
Oct 13, 2026Shikun Ban, Shohei Nagai, Qingyang Song +4Assistive Robotics Robot Imitation Learning
The Robotics Institute, School of Computer Science, Carnegie Mellon University Pittsburgh, USA · Intuitive Autonomy San Jose, USA
I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation
Oct 13, 2026Wensi Ai, Christopher Luey, Karthik Dharmarajan +7Robot Imitation Learning Interactive Imitation Learning
Stanford University · Lambda Inc.
Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning
Oct 13, 2026Yusuf Umut Ciftci, Joonwon Kang, Ajay Suresha Sathya +1Imitation Learning Robot Imitation Learning
University of Southern California · Stanford University · Georgia Institute of Technology +1
PrT-Flow: Probability-Tractable Flow Matching for Reinforcement Learning
Oct 13, 2026Congzhong Sun, Ning TanFlow Matching Flow-Based Generative Modeling
School of Computer Science and Engineering, Sun Yat-sen University
EgoCPT: Learning Dexterous Manipulation from a Single Ego-Centric Video with Contact-Aware Optimization
Oct 13, 2026Zhixin Sun, Kairui Shi, Tianao Ren +3Robotic Manipulation Robot Imitation Learning
Ant Group · Computer Science and Engineering, University at Buffalo · Mechanical Engineering, Stanford University
Smoothness Ranks Skill, Not Success: An Audit of Published Trajectory-Smoothness Curation Metrics Under Episode-Length Controls
Oct 12, 2026Jaiveer BassiRobot Imitation Learning Robotic Policy Evaluation
Independent Researcher
MIRROR: Reflecting Hidden Symmetries from Offline Robot Interaction Data
Oct 12, 2026Shiqi Liu, Yihang Yao, Peide Huang +2Robot Policy Learning Robot Imitation Learning
Carnegie Mellon University · Stanford University
Improving Policy Learning from Delayed Interventions
Oct 12, 2026Xiru Zhu, David Meger, Gregory DudekImitation Learning Robot Imitation Learning
McGill University, Computer Science
Value-Preserving Action Quantization
Oct 12, 2026Max Muchen Sun, Jueun Kwon, Benjamin Riviere +1Data-Efficient Robot Learning Robot Imitation Learning
Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA · Department of Mechanical and Aerospace Engineering and the Department of Computer Science, New York University, New York City, NY 10012, USA
CopyQuad: Quadrupedal Skill Transfer from a Single Side-View Video Without a Depth Model
Oct 12, 2026Mohamad H. Danesh, Raina Hu, Mengxue Xu +1Robot Imitation Learning Quadruped Locomotion
McGill University · Mila – Quebec AI Institute
Hand2Robot: Object-Centric 4D Hand Reconstruction from Human Demonstrations for Robotics
Oct 11, 2026Zetao Du, Weiqing Wang, Hanbing Zhang +4Robotic Manipulation 3D Reconstruction
ShanghaiTech University · InstAdapt · Institute of Automation, Chinese Academy of Sciences
Imitation Learning via Discrete and Rhythmic Primitive Composition
Oct 11, 2026Rafid Saif, Auke Ijspeert, Hsiu-Chin LinRobot Imitation Learning Robot Motion Generation
McGill University, and Mila - Quebec AI Institute, Canada. · École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.
Look Before You Move: Agent-Interface-Aligned Supervision for Open-World Mobile Manipulation
Oct 11, 2026Ronghe Qiu, Teli Ma, Jiaming Zhou +4Mobile Manipulation Language-Conditioned Robot Manipulation
The Hong Kong University of Science and Technology (Guangzhou)
Predict in Chunks, Revise in Spans: History-conditioned Action Regeneration for Robotic Manipulation
Oct 10, 2026Chenyang Wang, Yuntian Wang, Jiaying Tang +3Visuomotor Policy Learning Robot Imitation Learning
Soochow University, China · Tongji University, China
DexCast: Learning Real-World Dexterous Control from Human Videos
Oct 10, 2026Zihan Gao, Yuhong Zhang, Mengzhe Wang +7Robotic Manipulation Robot Imitation Learning
Tsinghua University · TsingTec · Fudan University +3
From Video to Vision: Learning Generalizable Humanoid Carrying from Human Motion
Oct 10, 2026Ziluo DingRL for Robotics Generalization in Robotic Manipulation
X-Humanoid
Splat2Act: From Captured Scenes to Personalized VLA Manipulation
Oct 9, 2026Xiaomeng Ye, Krishna Jaganathan, Gyubeum Lim +3Sim-to-Real Transfer Vision-Language-Action Models
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA. · Waymo LLC, Mountain View, CA, USA. · Toyota Research Institute, Cambridge, MA, USA.
LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos
Oct 9, 2026Zhiyuan Xiang, Peidong Liu, Mingyang Li +4Latent Action Learning Data-Efficient Robot Learning
Joy Future Academy, JD · School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen · Tsinghua SIGS, Tsinghua University +1
ATHLETE: Learning Reactive Humanoid Tennis via Trajectory-Guided Motion Matching
Oct 9, 2026Zhihao Tian, Zifan Wang, Enzheng Zhu +4Robot Policy Learning Humanoid Robot Control
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China.
Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models
Oct 9, 2026Min Lin, Bokai Zhou, Jianqi Lin +8Vision-Language-Action Models Robot Imitation Learning
Sun Yat-sen University · Yinwang Intelligent Technology Co., Ltd.
Human2Dex: Dexterous Robot Learning from Wrist-Centric Human Demonstrations
Oct 9, 2026Jiachen Zhang, Yuanyuan Li, Shaqi LuoRobotic Manipulation Cross-Embodiment Robot Learning
Beijing Academy of Artificial Intelligence, Beijing, China.
VICAR: Video Imitation with Contact-Aware Retargeting for Humanoid Interactive Skills
Oct 9, 2026Dvij Kalaria, Bike Zhang, S. Shankar SastryRobot Imitation Learning Humanoid Robotics
University of California, Berkeley
CeDR-Nav: Cross-Embodiment Mapless Navigation via Offline Demonstration Recasting
Oct 9, 2026Xianghui Wang, Xinming Zhang, Yiwei Qian +4Robot Navigation Cross-Embodiment Robot Learning
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong · College of Information Science and Technology, Eastern Institute of Technology, Ningbo, China · School of Computer Science and Technology, University of Science and Technology of China, Hefei, China +2

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LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rollouts

Wenxuan Guo, Yingping Liang, Zhaoguo Wang, Ziyuan Li+10

Tsinghua University · Dexmal · Loopeva

Robot Policy Learning Robot Policy Adaptation World Action Models Robot Imitation Learning

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Scaling expert demonstrations alone does not enable generalist robot policies to improve from deployment. Real-world rollouts contain successes, suboptimal behavior, and failures: imitating every recorded action can reinforce poor behavior, while filtering out failures discards evidence of physical interactions and their consequences. We introduce LoopWAM, a closed-loop world-action model that learns directly from this full quality spectrum, using rollouts from both itself and other deployed policies. Source-dependent noise sampling unifies behavior learning and action-conditioned world modeling, while quality steering and quality-weighted supervision retain full video supervision across qualities and regulate action imitation. Video and action tokens interact throughout parallel denoising, allowing learned consequences to inform action generation directly. On RoboChallenge Table30-V2, three deployment-and-training rounds raise success from 23.67% to 43.33%, a 19.7-percentage-point gain, with a final score of 56.80. LoopWAM also achieves 94.5% average success on RoboTwin~2.0 and 49.5% overall success on RoboCasa365 without additional embodied pretraining, obtaining the best overall results among the compared methods across all three benchmarks. On challenging shirt folding task, two rounds of deployment-driven improvement after single-task post-training raise success from 13.20% to 55.56%. These results demonstrate closed-loop improvement from mixed-quality experience under both multi-task learning and single-task post-training. orange{Project Page}