Imitation Learning

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Imitation learning teaches an agent to act by using examples of how a person or expert system behaves, often when specifying a reward is difficult. Behavior cloning directly maps observations to actions, while DAgger adds expert corrections on states the learner encounters, and inverse or adversarial imitation learning infers what behavior to match. Current methods also use diffusion policies, retrieval, world models, and visual, language, or tactile data for tasks such as robot manipulation, autonomous driving, and multi-agent coordination, with benchmarks including LIBERO, RoboMimic, and CARLA. A central challenge is that small errors can lead agents into states missing from demonstrations; improving recovery and safety while learning efficiently from limited, noisy, or conflicting examples remains an open tension.

Latest papers

263 papers37 papers in the last four weeks, up 32% on the four weeks before. 0.2% of all new papers.

InterReal: A Unified Physics-Based Imitation Framework for Learning Human-Object Interaction Skills
Mar 8, 2026Dayang Liang, Yuhang Lin, Xinzhe Liu +3Imitation Learning Robot Imitation Learning
Xiamen University · Zhejiang University · ShanghaiTech University +1
Latent Wasserstein Adversarial Imitation Learning
Mar 5, 2026Siqi Yang, Kai Yan, Alexander G. Schwing +1Imitation Learning Adversarial Imitation Learning
University of Illinois Urbana-Champaign
Bi-HIL: Bilateral Control-Based Multimodal Hierarchical Imitation Learning via Subtask-Level Progress Rate and Keyframe Memory for Long-Horizon Contact-Rich Robotic Manipulation
Mar 4, 2026Thanpimon Buamanee, Masato Kobayashi, Yuki UranishiLong-Horizon Robotic Manipulation Robot Imitation Learning
The University of Osaka · Kobe University
Inverse Contextual Bandits without Rewards: Learning from a Non-Stationary Learner via Suffix Imitation
Mar 4, 2026Yuqi Kong, Xiao Zhang, Weiran ShenContextual Bandits Imitation Learning
Gaoling School of Artificial Intelligence, Renmin University of China
GraspLDP: Towards Generalizable Grasping Policy via Latent Diffusion
Feb 26, 2026Enda Xiang, Haoxiang Ma, Xinzhu Ma +2Robotic Grasping Generalization in Robotic Manipulation
State Key Laboratory of Complex and Critical Software Environment, Beihang University, Beijing, 100191, China · School of Computer Science and Engineering, Beihang University, Beijing, 100191, China
Multi-agent imitation learning with function approximation: Linear Markov games and beyond
Feb 26, 2026Luca Viano, Till Freihaut, Emanuele Nevali +3Game Theory Imitation Learning
1EPFL · University of Zurich · 3Earth Species Project
Matching Multiple Experts: On the Exploitability of Multi-Agent Imitation Learning
Feb 24, 2026Antoine Bergerault, Volkan Cevher, Negar MehrImitation Learning Offline MARL
EPFL, UC Berkeley · EPFL · UC Berkeley
Inner Speech as Behavior Guides: Steerable Imitation of Diverse Behaviors for Human-AI coordination
Feb 24, 2026Rakshit Trivedi, Kartik Sharma, David C ParkesRobot Imitation Learning Imitation Learning
Massachusetts Institute of Technology · Georgia Institute of Technology · Harvard University
Beyond Mimicry: Toward Lifelong Adaptability in Imitation Learning
Feb 23, 2026Nathan Gavenski, Felipe Meneguzzi, Odinaldo RodriguesImitation Learning Agent Skill Learning
King’s College London, London, United Kingdom · University of Aberdeen & PUCRS, Aberdeen, United Kingdom
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
FAIL: Flow Matching Adversarial Imitation Learning for Image Generation
Feb 12, 2026Yeyao Ma, Chen Li, Xiaosong Zhang +2Flow Matching Conditional Image Generation
Shanghai Jiao Tong University · Xi’an Jiaotong University · Tencent
Mind the Gap: Rethinking I/O Design for Contact-Rich Visuomotor Policy Learning
Feb 9, 2026Cuijie Xu, Shurui Zheng, Zihao Su +7Contact-Rich Robotic Manipulation Robot Teleoperation
Department of Electronic Engineering, Tsinghua University · Qiuzhen College, Tsinghua University · School of Mechanical and Aerospace Engineering, Jilin University +2
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
When Does Predictive Inverse Dynamics Outperform Behavior Cloning?
Jan 29, 2026Lukas Schäfer, Pallavi Choudhury, Abdelhak Lemkhenter +10Imitation Learning Behavior Cloning
Microsoft · McGill University, Mila · Tsinghua University
Consistent Zero-Shot Imitation with Contrastive Goal Inference
Oct 20, 2025Kathryn Wantlin, Chongyi Zheng, Benjamin EysenbachContrastive Learning Reinforcement Learning
Department of Computer Science, Princeton University, Princeton, NJ, USA.
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
MIMIC-D: Multi-modal Imitation for MultI-agent Coordination with Decentralized Diffusion Policies
Sep 17, 2025Dayi Dong, Maulik Bhatt, Seoyeon Choi +1Multi-Agent Coordination Diffusion Policy
Department of Mechanical Engineering, University of California Berkeley, Berkeley, CA 94709, USA
Latent Policy Barrier: Learning Robust Visuomotor Policies by Staying In-Distribution
Aug 8, 2025Zhanyi Sun, Shuran SongVisuomotor Policy Learning Diffusion Policy
Stanford University
DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving
Jul 5, 2025Ziying Song, Lin Liu, Hongyu Pan +7End-to-End Autonomous Driving Diffusion-Based Planning
Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China · School of Artificial Intelligence (School of Software), Yanshan University, Qinhuangdao, China · Horizon Robotics +3
Where Do Humans Look When Demonstrating to Robots? Human Gaze Behavior in Pick-and-Place Tasks Across Demonstration Devices
Jun 6, 2025Yutaro Ishida, Takamitsu Matsubara, Takayuki Kanai +2Eye Tracking Imitation Learning
Toyota Motor Corporation, Japan. · Nara Institute of Science and Technology, Japan.
ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning
Jan 24, 2025Aleksandar Vujinovic, Aleksandar KovacevicRepresentation Learning World Model Learning
Faculty of Technical Sciences, University of Novi Sad
Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models
Sep 25, 2024Alexander Popov, Alperen Degirmenci, David Wehr +9End-to-End Autonomous Driving Imitation Learning
NVIDIA
Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees
May 26, 2024Yilei Chen, Vittorio Giammarino, James Queeney +1Adversarial Imitation Learning Imitation Learning
Division of Systems Engineering Boston University Boston, MA 02215, USA · Department of Computer Science Purdue University West Lafayette, IN 47907, USA · Amazon Robotics North Reading, MA 01864, USA +1

Mar 8, 2026J/K move · Enter open · S save

InterReal: A Unified Physics-Based Imitation Framework for Learning Human-Object Interaction Skills

Dayang Liang, Yuhang Lin, Xinzhe Liu, Jiyuan Shi+2

Xiamen University · Zhejiang University · ShanghaiTech University+1

Imitation Learning Robot Imitation Learning Humanoid Robot Control Human-Object Interaction Robotic RL

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Interaction is one of the core abilities of humanoid robots. However, most existing frameworks focus on non-interactive whole-body control, which limits their practical applicability. In this work, we develop InterReal, a unified physics-based imitation learning framework for Real-world human-object Interaction (HOI) control. InterReal enables humanoid robots to track HOI reference motions, facilitating the learning of fine-grained interactive skills and their deployment in real-world settings. Within this framework, we first introduce a HOI motion data augmentation scheme with hand-object contact constraints, and utilize the augmented motions to improve policy stability under object perturbations. Second, we propose an automatic reward learner to address the challenge of large-scale reward shaping. A meta-policy guided by critical tracking error metrics explores and allocates reward signals to the low-level reinforcement learning objective, which enables more effective learning of interactive policies. Experiments on HOI tasks of box-picking and box-pushing demonstrate that InterReal achieves the best tracking accuracy and the highest task success rate compared to recent baselines. Furthermore, we validate the framework on the real-world robot Unitree G1, which demonstrates its practical effectiveness and robustness beyond simulation.