Robot Imitation Learning

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Robot imitation learning studies how robots can acquire manipulation, navigation, locomotion, and whole-body skills from demonstrations rather than hand-coded rules or task rewards. Common approaches include behavior cloning with transformers or diffusion policies, interactive methods such as DAgger, inverse reinforcement learning, and hierarchical skill learning; demonstrations may come from teleoperation, human video, simulation, or robot-free interfaces. Research also uses visual, tactile, force, and proprioceptive feedback, with motion retargeting and data augmentation helping transfer skills across robots and settings. A central challenge is making policies reliable beyond their demonstrations: distribution shift, imperfect data, compounding errors, and sim-to-real gaps remain, alongside the need for efficient data collection and safe recovery.

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703 papers116 papers in the last four weeks, down 19% on the four weeks before. 0.8% of all new papers.

Generalizing from References using a Multi-Task Reference and Goal-Driven RL Framework
Feb 23, 2026Jiashun Wang, M. Eva Mungai, He Li +3Humanoid Robot Control Robot Policy Generalization
RAI Institute · Carnegie Mellon University
Universal Pose Pretraining for Generalizable Vision-Language-Action Policies
Feb 23, 2026Haitao Lin, Hanyang Yu, Jingshun Huang +5Robot Policy Learning Vision-Language-Action Models
Tencent Robotics X · Futian Laboratory · The Hong Kong University of Science and Technology +2
Human-to-Robot Interaction: Learning from Video Demonstration for Robot Imitation
Feb 22, 2026Thanh Nguyen Canh, Thanh-Tuan Tran, Haolan Zhang +3Learning from Demonstration Robot Imitation Learning
School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, 923-1211, Ishikawa, Japan. · University of Engineering and Technology, Vietnam National University, 10000, Hanoi, Vietnam. · Department of Robotics, Hanyang University, Ansan, 15588, Gyeonggi, Korea.
HeRO: Hierarchical 3D Semantic Representation for Pose-aware Object Manipulation
Feb 21, 2026Chongyang Xu, Shen Cheng, Haipeng Li +3Robotic Manipulation Robot Imitation Learning
College of Computer Science, Sichuan University · Dexmal · School of Information and Communication Engineering, University of Electronic Science and Technology of China
Learning Smooth Time-Varying Linear Policies with an Action Jacobian Penalty
Feb 20, 2026Zhaoming Xie, Kevin Karol, Jessica HodginsRL Control Robot Imitation Learning
Robotics AI Institute, Cambridge, Massachusetts, USA
WHED: A Wearable Hand Exoskeleton for Natural, High-Quality Demonstration Collection
Feb 20, 2026Mingzhang Zhu, Alvin Zhu, Jose Victor S. H. Ramos +9Dexterous Manipulation Robot Imitation Learning
Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USA. · Department of Computer Science, UCLA, Los Angeles, CA, USA. · Department of Electrical Engineering, UCLA, Los Angeles, CA, USA.
IRIS: Learning-Driven Task-Specific Cinema Robot Arm for Visuomotor Motion Control
Feb 19, 2026Qilong Cheng, Matthew Mackay, Ali BereyhiRobotic Control Vision-Based Robot Control
Mechanical and Aerospace Eng. Department, New York University · MIE Department, University of Toronto · ECE Department, University of Toronto
MeshMimic: Geometry-Aware Humanoid Motion Learning through 3D Scene Reconstruction
Feb 17, 2026Qiang Zhang, Jiahao Ma, Peiran Liu +20Terrain-Aware Robot Locomotion Robot Imitation Learning
X-Humanoid · The Hong Kong University of Science and Technology (Guangzhou) · The Australian National University +4
Feasibility-aware Imitation Learning from Observation with Multimodal Feedback
Feb 17, 2026Kei Takahashi, Hikaru Sasaki, Takamitsu MatsubaraImitation Learning Robot Imitation Learning
Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Ikoma-shi, Nara 630-0192, Japan
BPP: Long-Context Robot Imitation Learning by Focusing on Key History Frames
Feb 16, 2026Max Sobol Mark, Jacky Liang, Maria Attarian +4Robot Imitation Learning Robot Learning
Carnegie Mellon University · Google DeepMind · Princeton University
Learning Human-Like Badminton Skills for Humanoid Robots
Feb 9, 2026Yeke Chen, Shihao Dong, Xiaoyu Ji +12Humanoid Robot Control Robotic RL
Adaptive Robotic Controls Lab (ArcLab), Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China · EngineAI Robotics Technology Co., Ltd., Shenzhen, China
Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactions
Feb 7, 2026Yicheng Yang, Ruijiao Li, Lifeng Wang +7Reinforcement Learning Robotic RL
On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning
Feb 2, 2026Sacha Morin, Moonsub Byeon, Alexia Jolicoeur-Martineau +1Latent Action Learning Imitation Learning
Département d’informatique et de recherche opérationnelle, Université de Montréal · Mila – Quebec AI Institute · Samsung AI Lab, Montreal +1
HumanX: Toward Agile and Generalizable Humanoid Interaction Skills from Human Videos
Feb 2, 2026Yinhuai Wang, Qihan Zhao, Yuen Fui Lau +6Robot Skill Learning Humanoid Robotics
The Hong Kong University of Science and Technology · Shanghai AI Laboratory
Cross-Modal Visuo-Tactile Representation Learning with Action Chunking Transformers for Contact-Rich Manipulation
Jan 31, 2026Yaohua Liu, Rong Fu, Amir H. Gandomi +2Representation Learning Cross-Modal Representation Learning
Chinese Medicine Guangdong Laboratory, Hengqin, Zhuhai 519031, China · Institute of Collaborative Innovation, University of Macau, Macau 999078, China · Faculty of Engineering & IT, University of Technology Sydney, Ultimo, NSW 2007, Australia +3
CoLA-Flow Policy: Temporally Coherent Imitation Learning via Continuous Latent Action Flow Matching for Robotic Manipulation
Jan 30, 2026Wu Songwei, Jiang Zhiduo, Sun Wandong +4Flow Matching Latent Action Learning
Harbin Institute of Technology, Harbin 150001, China · Honor Device Co., Ltd., Shenzhen, China
OGPO: One-Step Generative Policy Optimization for Real-Time Robot Control
Jan 28, 2026Guowei Zou, Haitao Wang, Hejun Wu +3Robotic Control Real-Time Robot Control
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China
Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation
Jan 27, 2026Kaipeng Fang, Weiqing Liang, Yuyang Li +5Robot Skill Learning Bimanual Robotic Manipulation
University of Electronic Science and Technology of China · Southwest Jiaotong University · Tongji University +1
SHARE: Sharing Human Actions across Robot Embodiments through Unified Retargeting and Tracking
Jan 12, 2026Junzhe Xu, Haoyu Zhang, Yunheng Wang +6Human-to-Robot Motion Imitation Cross-Embodiment Robot Learning
JD Explore Academy Beijing, China
Learning Semantic Atomic Skills for Multi-Task Robotic Manipulation
Dec 20, 2025Yihang Zhu, Weiqing Wang, Shijie Wu +2Long-Horizon Robotic Manipulation Robot Skill Learning
ShanghaiTech University, Shanghai, China · InstAdapt
ISS Policy : Scalable Diffusion Policy with Implicit Scene Supervision
Dec 17, 2025Wenlong Xia, Jinhao Zhang, Ce Zhang +6Robot Skill Learning Visuomotor Policy Learning
[cs.RO]
AnchorDream: Repurposing Video Diffusion for Embodiment-Aware Robot Data Synthesis
Dec 12, 2025Junjie Ye, Rong Xue, Basile Van Hoorick +4Video Diffusion Models Synthetic Data Generation for Robotics
Toyota Research Institute · USC Physical Superintelligence (PSI) Lab
UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations
Dec 12, 2025Tingyu Yuan, Biaoliang Guan, Wen Ye +8Cross-Embodiment Robot Learning Robotic RL
CASIA · UCAS · XJTU +3
Learning Category-level Last-meter Navigation from RGB Demonstrations of a Single-instance
Dec 11, 2025Tzu-Hsien Lee, Fidan Mahmudova, Karthik DesinghRobot Skill Learning Robot Navigation
University of Minnesota, Twin Cities
MILE: A Mechanically Isomorphic Hand Exoskeleton and Visuotactile Robotic Hand for Data Collection in Dexterous Manipulation
Nov 29, 2025Jinda Du, Jieji Ren, Qiaojun Yu +6Robotic Hand Design Dexterous Manipulation
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. · Shanghai Key Laboratory of Intelligent Robotics, Shanghai Jiao Tong University, Shanghai 200240, China. · Shanghai Artificial Intelligence Laboratory, Shanghai, China. +1
SeFA-Policy: Fast and Accurate Visuomotor Policy Learning with Selective Flow Alignment
Nov 11, 2025Rong Xue, Jiageng Mao, Mingtong Zhang +1Rectified Flow Visuomotor Policy Learning
University of Southern California
PHUMA: Physically Reliable Humanoid Locomotion Dataset
Oct 30, 2025Kyungmin Lee, Sibeen Kim, Youngdo Lee +6Humanoid Robot Locomotion Robot Imitation Learning
KAIST
Few-Shot Demonstration-Driven Task Coordination and Trajectory Execution for Multi-Robot Systems
Oct 17, 2025Taehyeon Kim, Vishnunandan L. N. Venkatesh, Byung-Cheol MinRobot Motion Generation Imitation Learning
Purdue University, West Lafayette, IN, USA · Astemo Americas, Inc., Farmington Hills, MI, USA · Indiana University Bloomington, Bloomington, IN, USA
MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning
Oct 3, 2025Tianyu Xu, Jiawei Chen, Jiazhao Zhang +5Robot Navigation Visual Robot Navigation
Peking University, Beijing, China · Galbot, Beijing, China · Shanghai Jiao Tong University, Shanghai, China +1
ARMADA: Autonomous Online Failure Detection and Human Shared Control Empower Scalable Real-world Deployment and Adaptation
Oct 2, 2025Wenye Yu, Jun Lv, Zixi Ying +3Robot Policy Adaptation Robot Imitation Learning
Shanghai Jiao Tong University · Shanghai Innovation Institute · Noematrix Ltd.

Feb 23, 2026J/K move · Enter open · S save

Generalizing from References using a Multi-Task Reference and Goal-Driven RL Framework

Jiashun Wang, M. Eva Mungai, He Li, Jean Pierre Sleiman+2

RAI Institute · Carnegie Mellon University

Humanoid Robot Control Robot Policy Generalization Robot Imitation Learning Multi-Task RL Goal-Conditioned RL

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Learning agile humanoid behaviors from human motion offers a powerful route to natural, coordinated control, but existing approaches face a persistent trade-off: reference-tracking policies are often brittle outside the demonstration dataset, while purely task-driven Reinforcement Learning (RL) can achieve adaptability at the cost of motion quality. We introduce a unified multi-task RL framework that bridges this gap by treating reference motion as a prior for behavioral shaping rather than a deployment-time constraint. A single goal-conditioned policy is trained jointly on two tasks that share the same observation and action spaces, but differ in their initialization schemes, command spaces, and reward structures: (i) a reference-guided imitation task in which reference trajectories define dense imitation rewards but are not provided as policy inputs, and (ii) a goal-conditioned generalization task in which goals are sampled independently of any reference and where rewards reflect only task success. By co-optimizing these objectives within a shared formulation, the policy acquires structured, human-like motor skills from dense reference supervision while learning to adapt these skills to novel goals and initial conditions. This is achieved without adversarial objectives, explicit trajectory tracking, phase variables, or reference-dependent inference. We evaluate the method on a challenging box-based parkour playground that demands diverse athletic behaviors (e.g., jumping and climbing), and show that the learned controller transfers beyond the reference distribution while preserving motion naturalness. Finally, we demonstrate long-horizon behavior generation by composing multiple learned skills, illustrating the flexibility of the learned polices in complex scenarios.