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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UMI-Bridge: Action-Anchored Latent Alignment across Human and Robot Manipulation Data
Sep 16, 2026Haiyi Liu, Jinming Ma, Ke Rui +12Latent Action Learning Cross-Embodiment Robot Learning
Tsinghua University · Simple AI
Energy-Regularized Imitation Learning for Force- and Work-Aware Robotic Manipulation
Sep 16, 2026Toshiki Otani, Hiromu Taketsugu, Norimichi UkitaRobotic RL Robotic Manipulation
Toyota Technological Institute, Japan
Missing Bridges: Composition-Aware Active Imitation Learning
Sep 16, 2026Maxwell J. Jacobson, Ahmed H Qureshi, Yexiang XueImitation Learning Interactive Imitation Learning
Department of Computer Science, Purdue University West Lafayette, Indiana, USA
Vision-Language Grounded Task-Context-Aware Imitation Learning for Robotic Disassembly
Sep 15, 2026Jeon Ho Kang, Igal Tamarkin, Ethan Niu +2Long-Horizon Robotic Manipulation Language-Conditioned Robot Manipulation
Viterbi School of Engineering, University of Southern California, Los Angeles, USA
Dissecting Motion-Prior Regularization for Data-Scarce Robotic Insertion
Sep 15, 2026Ning Hu, Shuai Li, Jindong TanTrajectory Optimization Contact-Rich Robotic Manipulation
Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA · Engineering School of Sustainable Infrastructure and Environment, University of Florida, Gainesville, FL 32611, USA
XPACE: Joint World and Action Modeling from Heterogeneous Experience
Sep 15, 2026Jiacheng Wei, Jerry Bai, Xiaoyu Yue +13Cross-Embodiment Robot Learning Synthetic Data Generation for Robotics
World Model Team, XPENG Robotics
SWIM: Vision-Language-Grounded Soft Whole-Body Interactive Manipulation
Sep 15, 2026Tingcong Liu, Aye Phyu Phyu Aung, Junjie Xiong +4Language-Conditioned Robot Manipulation Whole-Body Robotic Manipulation
Nanyang Technological University, Singapore · Institute of Advanced Intelligence and Computing, A*STAR, Singapore · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates +1
The Latent That Never Was: A Forensic Re-run of the CVAE Ablation in Action Chunking Transformers
Sep 15, 2026Bo KangRobotic Policy Evaluation Robot Imitation Learning
Ghent University, Belgium
IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator
Sep 15, 2026Mehdi Heydari Shahna, Seihun Kim, Soyi Jung +3Robotic Control Robot Trajectory Tracking
Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland · Department of Defense Convergence Technology, Korea University, Seoul, Republic of Korea · Department of Electrical and Computer Engineering, Korea University, Seoul, Republic of Korea +2
Weave: Learning Whole-Body Dexterous Loco-Manipulation from Human-Object Interactions
Sep 15, 2026Liu Cao, Xingze Wu, Jingzhi Cui +4HOI Generation Humanoid Loco-Manipulation
1Tsinghua IIIS · 3Dalian University of Technology · 4The Chinese University of Hong Kong +1
UniDex-ViTac: Learning Unified Visuo-Tactile Dexterous Manipulation Policy from Human Video Data
Sep 15, 2026Hyesung Lee, Si-Hwan Heo, Sungwook YangRL for Robotics Visuomotor Policy Learning
Center for Humanoid Research, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea · Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Seoul 02455, Republic of Korea
Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing
Sep 14, 2026Matteo Grimaldi, David Klee, Ziling Chen +5Dexterous Manipulation Deformable Object Manipulation
Dexterous AI Group, Analog Devices, Inc.
Continuous Manifold-Decomposed Impedance Retargeting for Contact-Rich Imitation Learning
Sep 14, 2026Jiahao Liu, Kento Kawaharazuka, Tasuku Makabe +1Impedance Control Force-Controlled Robotic Manipulation
Department of Mechano-Informatics Graduate School of Information Science and Technology, The University of Tokyo Tokyo, Japan
Flow-Matched Motion Priors: Online Optimal-Transport Rewards for Imitation Learning
Sep 14, 2026Yilin Zou, Chenghua Liu, Chenglong Wu +1Imitation Learning RL for Legged Locomotion
School of Aerospace Engineering, Tsinghua University, Beijing 100084, China · Institute of Software, Chinese Academy of Sciences, Beijing, China
LieSpline-DP: Lie-Group B-Spline Diffusion Policy for Smooth Robot Manipulation
Sep 14, 2026Erxuan Xie, Bang Liu, Pingyun Nie +3Trajectory Generation Diffusion Policy
Shanghai Jiao Tong University
GIFT: Glove-Inferred Force Transfer: Force-Aware Human-to-Robot Skill Transfer from a Wearable Sensing Glove to a Robot Hand Without Tactile Sensors
Sep 12, 2026Tzach SarusiRobotic Grasping Robotic Manipulation
How to Better Train VLAs: Lessons Learned From the REAL-I Challenge at ICRA 2026
Sep 12, 2026Jiaming Wang, Jizhuo Chen, Diwen Liu +11Robot Policy Adaptation Robotic Policy Evaluation
Bi-MoDe: Bilateral Control-based Imitation Learning via Modifier-Conditioned Decoding for Modulation of Execution Speed and Contact Intensity
Sep 11, 2026Takumi Kobayashi, Masato Kobayashi, Yuki UranishiRobot Imitation Learning Robotic Control
Graduate School of Information Science and Technology, The University of Osaka · D3 Center, The University of Osaka · Graduate School of Maritime Sciences, Kobe University
Improving Imitation Learning Efficiency for Manipulation through Geometric Prior Pretraining
Sep 11, 2026Shogo Iwakata, Tomohiro Motoda, Ryosuke Yamada +8Cross-Embodiment Robot Learning Imitation Learning
Waseda University · Embodied AI Research Team, AIRC and National Institute of Advanced Industrial Science and Technology (AIST) · Computer Vision Research Team, AIRC and National Institute of Advanced Industrial Science and Technology (AIST) +3
SEED-UMI: Sharing the Exoskeleton between human and robot for onE-to-one Dexterous demonstration
Sep 10, 2026Tengbo Yu, Jiahao Wu, Daohan Li +4Contact-Rich Robotic Manipulation Robotic Data Collection
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University · Delta Intelligence
ObstaDiff: Generalizable Diffusion Policy Learning via Obstacle-aware Representations
Sep 10, 2026Jiawen Wang, Kevin Yao, Khalid JawedDiffusion-Based Planning Generalization in Robotic Manipulation
Department of Mechanical and Aerospace Engineering, University of California, Los Angeles · Department of Computer Science, University of California, Los Angeles
HuRo: Robotizing Human Videos for Scalable VLA Pretraining
Sep 9, 2026Jinho Jeong, Se June Joo, Jaehyun Kang +4Cross-Embodiment Robot Learning Vision-Language-Action Models
RLWRLD · Yonsei University
RoboDrop: Curating VLA Post-Training Data via Local Gradient Compatibility
Sep 9, 2026Runze Xu, Yuanfan Xu, Cuijie Xu +4Robotic Data Collection Training Data Selection
Tsinghua University · Striding AI
Proxy Policy Steering
Sep 8, 2026Chuanruo Ning, Tianrui Wang, Wei-Chiu Ma +1Robot Policy Adaptation Robot Foundation Models
Cornell University
FOCI Policy: Focus on Object-Centric Interactions for Relational Manipulation Policies
Sep 8, 2026Ze Fu, Pinhao Song, Yutong Hu +1Generalization in Robotic Manipulation Robotic Manipulation
KU Leuven, Dept. Mechanical Engineering, Research unit Robotics, Automation and Mechatronics · KU Leuven, Dept. Electrical Engineering, Research unit Processing Speech and Images
Dex-X: Learning Visual-Tactile Dexterous Manipulation From Human Videos with Simulated Interaction
Sep 7, 2026Ruoqu Chen, Feixiang Ruan, Liu Cao +9Sim-to-Real Transfer Robotic Manipulation
Tsinghua University · Shanghai Qizhi Institute · Sharpa +2
CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements
Sep 7, 2026Hongxiang Zhao, Mutian Xu, Zeyu Jin +3Robotic Data Collection Robotic Manipulation
SSE, CUHKSZ · GenuX · FNii-Shenzhen
Distributed Dexterous Manipulation with Spatially Conditioned Multi-Agent Transformers
Sep 7, 2026Sarvesh PatilRobotic Manipulation Multi-Robot Coordination
Carnegie Mellon University, The Robotics Institute Pittsburgh, PA, United States
ContextFlow: In-Context Flow Matching for Robot Manipulation
Sep 6, 2026Jian Ding, Xianjie Dai, Roei Herzig +5ICL for Robotics Robot Imitation Learning
KAUST, Thuwal, Saudi Arabia · University of California, Berkeley, USA · Zurich, Switzerland
Unifying Physics-Based Humanoid Interaction with a Context-Conditioned Interaction Prior
Sep 6, 2026Jianan Li, Xiao Chen, Tien-Tsin WongHumanoid Robot Control Robot Skill Learning
The Chinese University of Hong Kong, Department of Computer Science and Engineering, Hong Kong, China · The Chinese University of Hong Kong, Department of Information Engineering, Hong Kong, China · Monash University, Department of Data Science and AI, Clayton, Australia

Sep 16, 2026J/K move · Enter open · S save

UMI-Bridge: Action-Anchored Latent Alignment across Human and Robot Manipulation Data

Haiyi Liu, Jinming Ma, Ke Rui, Yuteng Wei+11

Tsinghua University · Simple AI

Latent Action Learning Cross-Embodiment Robot Learning Transfer Learning Robot Imitation Learning

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Real-robot demonstrations are limited, motivating the use of human manipulation data collected without robots, including egocentric videos and handheld Universal Manipulation Interface (UMI) demonstrations. However, differences in viewpoint, embodiment, and available action supervision make it difficult to align representations across these sources according to manipulation motion rather than visual appearance. We introduce UMI-Bridge, which uses UMI as an intermediate domain to align representations according to action equivalence rather than pixel similarity. UMI action supervision anchors the latent representation to end-effector motion and gripper behavior, while synchronized head-wrist observations and paired ego-UMI clips support alignment across views and domains. We train a dual-view latent action model (LAM) on human manipulation data without robot demonstrations, then freeze its wrist teacher and dynamics model to regularize vision-language-action (VLA) post-training on UMI and robot data. The shared wrist interface enables this training-time supervision across both domains while preserving the policy's standard inference architecture. Across three real-robot tasks, UMI-Bridge achieves 91.7% mean success versus 73.3% for Naive Co-training with matched UMI and robot data. On two data-efficiency tasks, it surpasses a full-data Robot-only baseline using 25% of the robot demonstrations together with UMI data. It also achieves 85% and 90% success on two additional tasks learned from UMI demonstrations without task-specific robot demonstrations. These results support action-anchored latent alignment for data-efficient robot learning and UMI-to-robot task transfer.