Robot Imitation Learning

Latest papers 647

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

Tri-Manual Visuomotor Imitation Learning of Robot Policies
Jul 28, 2026James Zhao, Mingyuan Ba, Weiming ZhiRobot Teleoperation Visuomotor Policy Learning
School of Computer Science and 2Australian Center For Robotics, The University of Sydney, Australia · College of Connected Computing, Vanderbilt University, TN, USA
KAI: A Kinematic-Aware Interface for Data-Efficient Articulated Object Manipulation
Jul 27, 2026Yaping Li, Zhaxizhuoma, Qiaojun Yu +3Robot Skill Learning Generalization in Robotic Manipulation
The Chinese University of Hong Kong · Shanghai AI Laboratory · Shanghai Jiao Tong University
DeVA: Decoupled Video-Action Model with physical guidance for robot policy learning
Jul 27, 2026Mengqi Zhang, Sahil Khose, Simar Kareer +3Visuomotor Policy Learning Vision-Language-Action Models
University of California, Irvine · Georgia Institute of Technology
Learning Reusable Hybrid Motion Priors for Humanoid Locomotion from Motion Imitation
Jul 27, 2026Valerio Belli, Valerio Modugno, Enrico Mingo Hoffman +1RL for Robotics Latent Action Learning
UNIROMA, UCL · Sapienza University of Rome, Rome, Italy · University College London, London, UK +3
PRISM: Polynomial Representations for Interaction-Structured Motor Control
Jul 26, 2026Seung Hyun Lee, Stella X. YuRobotic Control RL for Robotics
University of Michigan, Ann Arbor Computer Science and Engineering
The Curse of Precision: A Data Scaling Law for High-Precision Robotic Manipulation
Jul 25, 2026Cuijie Xu, Yuanfan Xu, Min Xue +5Robotic Manipulation Robot Imitation Learning
Department of Electronic Engineering, Tsinghua University, Beijing, China. · OpenMind (WuHu) Robotics Co., Ltd., Wuhu, China. · Institute for Embodied Intelligence and Robotics, Tsinghua University.
GuidedAttention: Interpretable and Correctable Visual Attention for OOD-Robust Robot Manipulation via Imitation Learning
Jul 23, 2026Masaki Murooka, Ryoichi Nakajo, Keisuke Shirai +4Visual Attention Robot Skill Learning
CNRS-AIST JRL (Joint Robotics Laboratory), IRL, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8560, Japan · Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan
Emergent Compositional Skills in Mixture-of-Experts VLAs
Jul 22, 2026Shlok Shah, Rhiaan Jhaveri, Tharun Kumar Tiruppali Kalidoss +3Robot Policy Learning Robot Skill Learning
Department of Computer Science, Princeton University, Princeton, NJ, USA · Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA
Robots Acquire Manipulation Skills in Seconds from a Single Human Video
Jul 22, 2026Guangyan Chen, Meiling Wang, Te Cui +9Continual Robot Learning Robot Skill Learning
Beijing Institute of Technology · X SQUARE ROBOT · Tsinghua University
What Matters in Humanoid General Motion Tracking? An Empirical Study
Jul 22, 2026Fabio Amadio, Enrico Mingo HoffmanHumanoid Robot Control Robotic Policy Evaluation
HUCEBOT · Inria, Université de Lorraine, CNRS, Nancy, France
EA-Nav: Learning Safe Visual Navigation Policies with Embodiment Awareness
Jul 22, 2026Jialu Zhang, Yong Du, Xianda Guo +6Embodied Navigation Cross-Embodiment Robot Learning
Zhejiang University · Hangzhou, Zhejiang, China · Wuhan University +4
Learning Diverse Humanoid Tasks via Synthetic Video Scenarios without Real World Data
Jul 22, 2026Yun-Hao Tsai, Cong-Thanh Vu, Yen-Chen LiuSynthetic Data Generation for Robotics Learning from Demonstration
Department of Mechanical Engineering, National Cheng Kung University (NCKU), Tainan 70101, Taiwan
EgoRecovery: Acquiring Failure Recovery Ability Through Human Recovery Demonstration
Jul 22, 2026Zuhao Ge, Yuchen Zhou, Weitao Zhou +8Robot Failure Recovery Cross-Embodiment Robot Learning
Institute of Trustworthy Embodied AI (TEAI), Fudan University · 2Shanghai Key Laboratory of Multimodal Embodied AI · 3Simple AI
Imitation of Arm Gestures by the Semi-Humanoid Robot NICO
Jul 20, 2026Anastasiya Ihnatovich, Igor FarkašHumanoid Robot Control Robot Imitation Learning
Faculty of Mathematics, Physics and Informatics, Comenius University Bratislava, Mlynská dolina 6284, 842 48 Bratislava, Slovakia
Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning
Jul 20, 2026Ziyi Liu, Grace ZhangReinforcement Learning Learning from Demonstration
University of Southern California
Dynamics-Aware Meta-Imitation for Generalization to Unseen Robotic Manipulation
Jul 17, 2026Zhenduo Shang, Xiyao Liu, Bohan Li +4Robot Skill Learning Generalization in Robotic Manipulation
State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China · University of Chinese Academy of Sciences, Beijing 100049, China · Shenyang University of Technology
Industrial Dexterity Benchmark: A Hardware-Software Benchmarking Platform for Industrial Dexterous Manipulation
Jul 15, 2026Honglu He, Jacob Laufer, Zhiwu Zheng +8Dexterous Manipulation Diffusion Policy
Analog Devices, Inc., USA
Reverse to Advance: Teleoperation-Cost Effective Hard Policy Learning from Reversed Easy Tasks
Jul 15, 2026Qiyuan Qiao, Ge Yuan, Can Wang +1Robot Teleoperation Robotic Manipulation
The University of Hong Kong
Worlds in One Demo: A Synthetic Data Engine for Learning Open-World Mobile Manipulation
Jul 14, 2026Lingxiao Guo, Huanyu Li, Guanya ShiLong-Horizon Robotic Manipulation Mobile Manipulation
Carnegie Mellon University
A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
Jul 13, 2026Yunhai Feng, Natalie Leung, Jiaxuan Wang +3Robot Skill Learning Contact-Rich Robotic Manipulation
Cornell University · Amazon FAR
WALA Learning Executable Latent Actions from Action-Labeled Demonstrations and Action-Free Videos
Jul 13, 2026Jiahao Liu, Yupeng Zheng, Zhongpu Xia +10Latent Action Learning Robot Policy Learning
CASIA · Anyverse Dynamics · UCAS +2
Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation
Jul 13, 2026Haojie Huang, Linfeng Zhao, Haotian Liu +9Visuomotor Policy Learning Robotic Manipulation
1Northeastern University · 2Stanford University · University of Pennsylvania +2
PAKE: Learning Whole-Body Loco-Manipulation with Partial Kinematic Embeddings
Jul 13, 2026Zhengmao He, Moonkyu Jung, Hyeongjun Kim +4Normalizing Flows Whole-Body Robotic Manipulation
The Hong Kong University of Science and Technology (Guangzhou). · Korea Advanced Institute of Science and Technology. · ETH Zurich. +1
SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation
Jul 13, 2026Haidong Cao, Wenjun Cao, Quanhao Li +5Long-Horizon Robotic Manipulation Visuomotor Policy Learning
Institute of Trustworthy Embodied AI, Fudan University, China · Shanghai Key Laboratory of Multimodal Embodied AI, China
Dual-Process Atomic Skill Learning: Decoupling Semantic Reasoning and Real-Time Control
Jul 12, 2026Jun Chen, Erdemt Bao, Wenlong Dong +7Robot Skill Learning Imitation Learning
University of Electronic Science and Technology of China · Huazhong University of Science and Technology · Southern University of Science and Technology +2
Source-Lifted Flow Matching for Intervenable Multimodal Imitation
Jul 11, 2026He Zhang, Ying Sun, Ziyang Chen +6Robotic Control Flow Matching
The Hong Kong University of Science and Technology (Guangzhou) · AI2 Robotics
More Structure, Not More Capacity: Object-Centric Representations for Visuomotor Imitation Learning
Jul 10, 2026Yi Li, Alexandre Chapin, Liming Chen +2Robot Skill Learning Visuomotor Policy Learning
TU Darmstadt · 1TU Darmstadt · LIRIS +3
Implicit Behavior Coordination from Sub-Task Demonstrations by Exploiting Overlap-Induced Multimodality
Jul 10, 2026Ahmed Shokry, Usama Ahmed Siddiquie, Sicong Pan +1Robot Policy Learning Long-Horizon Robotic Manipulation
Humanoid Robots Lab and Center for Robotics, University of Bonn, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Bonn, Germany
TactiDex: A Real-World Tactile-Guided Benchmark for Human-Like Dexterous Manipulation
Jul 10, 2026Suting Ni, Hanbing Zhang, Zhenyu Wei +4Dexterous Manipulation Bimanual Robotic Manipulation
Shanghaitech University · InstAdapt
ContactMimic: Humanoid Object Interaction via Contact Control
Jul 9, 2026Xinyao Li, Xialin He, Runpei Dong +1Robotic Control Robot Skill Learning
University of Illinois Urbana-Champaign

Jul 28, 2026J/K move · Enter open · S save

Tri-Manual Visuomotor Imitation Learning of Robot Policies

James Zhao, Mingyuan Ba, Weiming Zhi

School of Computer Science and 2Australian Center For Robotics, The University of Sydney, Australia · College of Connected Computing, Vanderbilt University, TN, USA

Robot Teleoperation Visuomotor Policy Learning Robot Motion Generation Robotic Manipulation Robot Imitation Learning

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Bimanual teleoperation provides an effective way to collect robot demonstrations, but it assumes that the operator and robot have matching numbers of simultaneous control channels. This assumption breaks for tri-manual systems: the robot can coordinate three arms concurrently, whereas a single operator can continuously control only two. Pairwise mode switching may therefore record otherwise independent motions sequentially, causing behaviour cloning to reproduce delays imposed by the interface rather than required by the task. We present TriManPolicy, a tri-manual imitation learning system that allows one operator to demonstrate behaviours for three arms. Its central component is Dependency-Aware Tri-Arm Scheduling (DATS). The key idea is to preserve the demonstrated arm motions while reconsidering when they occur. DATS retimes demonstrations offline by preserving local sensorimotor segments of fixed duration and repositioning them according to constraints on task order and arm usage that are reviewed by a human. The resulting data train a single synchronous policy for all three arms, while deployment requires neither the dependency graph nor the scheduler. Across six challenging tasks performed in the real world, policies trained on demonstrations retimed by DATS exhibit more efficient coordination while maintaining comparable observed task success. Offline analysis further shows that DATS changes the supervision across arms rather than merely removing idle periods. Project videos and additional material are available at https://aus.bot/trimanpolicy/.