Imitation Learning

Follow this topic Ask about this topic

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.

Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation
Jun 2, 2026Yilong Wang, Cheng Qian, Edward JohnsDeformable Object Manipulation Generalization in Robotic Manipulation
The Robot Learning Lab Imperial College London
CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization
Jun 2, 2026Tewodros Ayalew, Matthew Jeung, Samuel Wheeler +5Latent Action Learning Goal-Conditioned Planning
University of Chicago · 3Argonne National Laboratory · 2Toyota Technological Institute at Chicago
Reinforcement Learning from Cross-domain Videos with Video Prediction Model
Jun 2, 2026Zhao Yang, Xinrui Zu, Jacob E. Kooi +5RL for Robotics Imitation Learning
VU Amsterdam, The Netherlands · ISAE-SUPAERO, France
Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards
Jun 1, 2026Christian Scherer, Joe Watson, Theo Gruner +3Reinforcement Learning Robot Skill Learning
Technical University of Darmstadt · Zuse School ELIZA · University of Oxford +2
Training-Free Imitation Learning with Closed-Form Diffusion Policies
May 31, 2026Raghav Mishra, Ian R. ManchesterDiffusion Model Guidance Visuomotor Policy Learning
Australian Center for Robotics, ARIAM Hub, and School of Aerospace, Mechanical and Mechatronic Engineering University of Sydney
Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry
May 31, 2026Zemin Yang, Yaoyu He, Yiming Zhong +5Robot Policy Learning Imitation Learning
ShanghaiTech University · Shanghai Jiao Tong University · The Chinese University of Hong Kong +1
Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs
May 31, 2026Jianing Qian, Qinhe Peng, Emmanuel Panov +4Long-Horizon Robotic Manipulation Imitation Learning
University of Pennsylvania · RAI Institute · University of Michigan
Infeasible optimization problems and the hierarchical augmented Lagrangian method in imitation learning
May 30, 2026Roland Andrews, Justin Carpentier, Ajay SathyaImitation Learning Augmented Lagrangian Method
SWIM: Single-Instance Whole-Body Imitation for swiMming
May 29, 2026Binglun Wang, Edmond S. L. Ho, He WangImitation Learning
University College London · University of Glasgow
Phase-Conditioned Imitation Learning with Autonomous Failure Recovery for Robust Deformable Object Manipulation
May 28, 2026Dayuan Chen, Kai Tang, Yukuan Zhang +2Bimanual Robotic Manipulation Deformable Object Manipulation
Department of Robotics, Tohoku University, Sendai, Miyagi, Japan · JC STEM Lab of Robotics for Soft Materials, the Department of Electrical and Computer Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong SAR, China
Teacher-Student Representational Alignment for Reinforcement Learning-Driven Imitation Learning
May 27, 2026Meraj Mammadov, Pedro Zuidberg Dos Martires, Johannes Andreas StorkImitation Learning Robot Imitation Learning
Department of Computer Science Örebro University
SPAR: Support-Preserving Action Rectification
May 27, 2026Jiaxin Zhao, Weihang Pan, Xun Liang +1Imitation Learning Offline RL
Zhejiang University.
HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning
May 26, 2026Kevin Lin, Ajay Mandlekar, Caelan Reed Garrett +7Humanoid Loco-Manipulation Synthetic Data Generation for Robotics
1NVIDIA · 2The University of Texas at Austin
Adversarial Dual On-Policy Distillation from Expressive Teacher
May 26, 2026Zhenglin Wan, Jingxuan Wu, Xingrui Yu +5Robot Policy Learning Adversarial Imitation Learning
School of Computing, National University of Singapore · Department of Statistics and Operations Research, UNC-Chapel Hill, America · CFAR, Agency for Science, Technology and Research, Singapore +4
Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets
May 26, 2026Ashima Khanna, Dominik GrimmSurrogate-Assisted Optimization Black-Box Optimization
Bioinformatics, Technical University of Munich, TUM Campus Straubing, 94315 Straubing, Germany · University of Applied Sciences Weihenstephan-Triesdorf, 94315 Straubing, Germany
On the Generalization Capabilities, Design Choices and Limitations of Keypoint Imitation Learning
May 26, 2026Thomas Lips, Marco Moletta, Michael C. Welle +2Generalization in Robotic Manipulation Imitation Learning
AI and Robotics Lab, IDLAB-AIRO, Ghent University-imec · Robotics, Perception and Learning Lab, (RPL), EECS, KTH Royal Institute of Technology. · INCAR Robotics AB, Stockholm, Sweden.
How to Mitigate the Distribution Shift Problem in Robotics Control: A Robust and Adaptive Approach Based on Offline to Online Imitation Learning
May 25, 2026Hyung-Suk Yoon, Seung-Woo SeoDistribution Shift Robustness Imitation Learning
Department of Electronic and Computer Engineering, Seoul National University, Seoul, South Korea.
Understanding Multimodality in Generative Behavioral Cloning
May 21, 2026Lorenzo Mazza, Massimiliano Datres, Ariel Rodriguez +3Latent Variable Models Imitation Learning
NCT/UCC Dresden, UKDD Dresden, TU Dresden, DKFZ Heidelberg · Ludwig-Maximilians-Universität München, Munich Center for Machine Learning (MCML) · BMFTR Research Hub 6G-Life +4
Demo-JEPA: Joint-Embedding Predictive Architecture for One-shot Cross-Embodiment Imitation
May 20, 2026Jingyang He, Guangrun Li, Jieyu Zhang +3Robot Skill Learning World Models for Robotics
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · Beijing Innovation Center of Humanoid Robotics · University of Washington
Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives
May 19, 2026Junli Wang, Zhihua Hua, Xueyi Liu +7End-to-End Autonomous Driving Imitation Learning
Institute of Automation, Chinese Academy of Sciences · Xiaomi Embodied Intelligence Team · Fudan University
From a Single Demonstration to a General Policy for Contact-Rich Manipulation
May 17, 2026Xing Li, Oliver BrockRobot Skill Learning Contact-Rich Robotic Manipulation
Robotics and Biology Laboratory, Technische Universität Berlin · Technische Universität Berlin · Science of Intelligence, Research Cluster of Excellence, Berlin +1
From Imitation to Interaction: Mastering Game of Schnapsen with Shallow Reinforcement Learning
May 16, 2026Ján Klačan, Sizhong ZhangReinforcement Learning Game-Playing Agents
Vrije Universiteit Amsterdam
When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited
May 16, 2026Rishabh Agrawal, Rahul Jain, Ashutosh NayyarImitation Learning Interactive Imitation Learning
University of Southern California
Imitation learning for clinical decision support in pediatric ECMO
May 15, 2026Fateme Golivand, Michael Skinner, Saurabh Mathur +5Latent Action Learning Clinical Decision Support
University of Texas at Dallas · Technische Universität Darmstadt · Swarthmore College +1
HoloMotion-1 Technical Report
May 14, 2026Maiyue Chen, Kaihui Wang, Bo Zhang +7Robot Foundation Models Imitation Learning
Horizon Robotics
Hand-in-the-Loop: Improving VLA Policies for Dexterous Manipulation via Seamless Hand-Arm Intervention
May 14, 2026Zhuohang Li, Liqun Huang, Wei Xu +5Long-Horizon Robotic Manipulation Bimanual Robotic Manipulation
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University · Shanghai Key Laboratory of Intelligent Robotics, Meta Robotics Institute, Shanghai Jiao Tong University, Shanghai 200240, China · ByteDance Seed +1
Ergodic Imitation for Adaptive Exploration around Demonstrations
May 13, 2026Ziyi Xu, Cem Bilaloglu, Yiming Li +1Imitation Learning Learning from Demonstration
Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland · Idiap Research Institute, Martigny, Switzerland
X-Imitator: Spatial-Aware Imitation Learning via Bidirectional Action-Pose Interaction
May 12, 2026Kai Xiong, Hongjie Fang, Lixin Yang +1Robot Skill Learning Visuomotor Policy Learning
Shanghai Jiao Tong University
Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation
May 12, 2026Ziyad Sheebaelhamd, Luca Viano, Volkan Cevher +1Multi-Objective Reinforcement Learning Imitation Learning
University of Tübingen · EPFL · University of Technology Nuremberg
NavOL: Navigation Policy with Online Imitation Learning
May 12, 2026Xiaofei Wei, Chun Gu, Li ZhangVisual Navigation Robot Navigation
School of Data Science, Fudan University · 2Shanghai Innovation Institute.

Jun 2, 2026J/K move · Enter open · S save

Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation

Yilong Wang, Cheng Qian, Edward Johns

The Robot Learning Lab Imperial College London

Deformable Object Manipulation Generalization in Robotic Manipulation Imitation Learning Robot Imitation Learning

Open paper AskPDF arXiv

Deformable object manipulation (DOM) is challenging due to high-dimensional, partially observable states that evolve through long-horizon, topology-changing interactions with multiple valid manipulation modes. We introduce Instant-Fold, an in-context imitation learning framework for DOM. Given a single human demonstration, our policy infers and executes diverse manipulation modes directly from the demonstration, including variations in spatial execution and ordering, without requiring gradient updates. Our approach first learns deformation-aware visual representations via temporal contrastive pretraining, after which a flow-matching transformer policy conditioned on the demonstration predicts actions to execute the intended manipulation mode. Trained entirely in simulation, Instant-Fold generalizes across diverse folding modes and transfers zero-shot to real-world settings without additional data collection or finetuning. Videos are available at https://instant-fold.github.io.