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.

Coordinated Diffusion: Generating Multi-Agent Behavior Without Multi-Agent Demonstrations
May 12, 2026Lasse Peters, Laura Ferranti, Andrea Bajcsy +1Multi-Agent Coordination Diffusion Policy
University of California, Berkeley · Delft University of Technology · Carnegie Mellon University
Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates
May 10, 2026Anish Diwan, Davide Tateo, Christopher E. Mower +3Reinforcement Learning Policy Optimization
Technical University of Darmstadt · Robotics Institute Germany (RIG) · Lund University +3
Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
May 9, 2026Surbhi Goel, Jonathan Pei, James WangImitation Learning Learning from Demonstration
Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuning
May 9, 2026Minmin Zhang, Sina Aghaei, Soroush SaghafianSupervised Fine-Tuning Sequential Decision Making
Harvard University
A Visuo-Tactile Data Collection System with Haptic Feedback for Coarse-to-Fine Imitation Learning
May 9, 2026Yeseung Kim, Nayoung Oh, Jun Park +2Haptic Feedback Imitation Learning
Korea Advanced Institute of Science and Technology, Yuseong-gu Daehak-ro 291, Daejeon 34141, South Korea
Zero-shot Imitation Learning by Latent Topology Mapping
May 8, 2026Maxwell J. Jacobson, Yexiang XueImitation Learning Long-Horizon Agent Tasks
Purdue University, Department of Computer Science, West Lafayette, IN
Neurosymbolic Imitation Learning with Human Guidance: A Privileged Information Approach
May 8, 2026Nikhilesh Prabhakar, Varun Balaji, Athresh Karanam +2Imitation Learning Neurosymbolic Learning
Department of Computer Science, The University of Texas at Dallas, Richardson TX 75080, USA · Computer Science Department and Centre for Cognitive Science, TU Darmstadt, Germany
Process Matters more than Output for Distinguishing Humans from Machines
May 7, 2026Milena Rmus, Mathew D. Hardy, Thomas L. Griffiths +1AI Agent Evaluation Imitation Learning
Roundtable Technologies Inc. · Princeton University
Provable imitation learning for control of instability in partially-observed Vlasov--Poisson equations
May 6, 2026Xiaofan Xia, Qin Li, Wenlong MouImitation Learning
Department of Statistical Sciences, University of Toronto† · Department of Mathematics, University of Wisconsin–Madison⋄
Stage Light is Sequence $^2$ : Multi-Light Control via Imitation Learning
May 5, 2026Zijian Zhao, Dian Jin, Zijing Zhou +1Imitation Learning
The Hong Kong University of Science and Technology · The Hong Kong Polytechnic University · The University of Hong Kong +1
Real-Time Evaluation of Autonomous Systems under Adversarial Attacks
May 5, 2026Adithya Mohan, Xujun Xie, Venkatesh Thirugnana Sambandham +1Autonomous Driving Safety Evaluation Adversarial Robustness
AI Motion Institute, Technische Hochschule Ingolstadt, Germany
Population-Aware Imitation Learning in Mean-field Games with Common Noise
May 5, 2026Grégoire Lambrecht, Mathieu LaurièreMean Field Games Adversarial Imitation Learning
Center for Data Science, New York University and NYU Shanghai, New York, United States of America · Shanghai Center for Data Science; NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai; NYU Shanghai, Shanghai, People’s Republic of China.
Adversarial Imitation Learning with General Function Approximation: Theoretical Analysis and Practical Algorithms
May 3, 2026Tian Xu, Zhilong Zhang, Zexuan Chen +3Reinforcement Learning Adversarial Imitation Learning
National Key Laboratory for Novel Software Technology and School of Artificial Intelligence, Nanjing University · School of Mathematics, Nanjing University
An Efficient Metric for Data Quality Measurement in Imitation Learning
May 2, 2026Noushad Sojib, Momotaz BegumImitation Learning Robot Imitation Learning
Authors are with the Department of Computer Science, University of New Hampshire, USA
A Principled Approach for Creating High-fidelity Synthetic Demonstrations for Imitation Learning
May 2, 2026Moniruzzaman Akash, Momotaz BegumSynthetic Data Generation for Robotics Robot Motion Generation
Department of Computer Science · University of New Hampshire
To Do or Not to Do: Ensuring the Safety of Visuomotor Policies Learned from Demonstrations
May 2, 2026Riad Ahmed, Moniruzzaman Akash, Momotaz BegumVisuomotor Policy Learning Imitation Learning
Department of Computer Science University of New Hampshire
TAIL-Safe: Task-Agnostic Safety Monitoring for Imitation Learning Policies
May 2, 2026Riad Ahmed, Momotaz BegumImitation Learning Robot Imitation Learning
University of New Hampshire
Tube Diffusion Policy: Reactive Visual-Tactile Policy Learning for Contact-rich Manipulation
Apr 26, 2026Teng Xue, Alberto Rigo, Bingjian Huang +4Robot Policy Learning Contact-Rich Robotic Manipulation
Meta Reality Labs Research · Idiap Research Institute · École Polytechnique Fédérale de Lausanne (EPFL) +2
Learning from the Best: Smoothness-Driven Metrics for Data Quality in Imitation Learning
Apr 24, 2026Soham Kulkarni, Raayan Dhar, Yuchen CuiImitation Learning Robot Imitation Learning
Department of Computer Science, University of California, Los Angeles, CA 90095, USA
GCImOpt: Learning efficient goal-conditioned policies by imitating optimal trajectories
Apr 24, 2026Jon Goikoetxea, Jesús F. PalaciánRobotic Control Imitation Learning
Department of Statistics, Mathematics and Computer Science, Public University of Navarre · Ensimag, Institut National Polytechnique de Grenoble, Universit´e Grenoble Alpes · Institute for Advanced Materials and Mathematics (InaMat2)
DeVI: Physics-based Dexterous Human-Object Interaction via Synthetic Video Imitation
Apr 22, 2026Hyeonwoo Kim, Jeonghwan Kim, Kyungwon Cho +2Imitation Learning Robotic Manipulation
Seoul National University · RLWRLD
Zero-Shot Scalable Resilience in UAV Swarms: A Decentralized Imitation Learning Framework with Physics-Informed Graph Interactions
Apr 17, 2026Huan Lin, Lianghui DingMulti-Robot Systems Swarm Robotics
Institute of Image Communication and Network Engineering, School of Integrated Circuits, Shanghai Jiao Tong University, Shanghai 200240, China
R3D: Revisiting 3D Policy Learning
Apr 16, 2026Zhengdong Hong, Shenrui Wu, Haozhe Cui +83D ViTs Visuomotor Policy Learning
Zhejiang University · ShanghaiTech University
A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics
Apr 16, 2026Fawad Javed Fateh, Ali Shah Ali, Murad Popattia +4ICL for Robotics Vector Quantization
Retrocausal, Inc., Redmond, WA 98052, USA
Active Imitation Learning for Thermal- and Kernel-Aware LFM Inference on 3D S-NUCA Many-Cores
Apr 13, 2026Yixian Shen, Chaoyao Shen, Jan Deen +3LLM Inference Scheduling Imitation Learning
University of Amsterdam, Netherlands · Southeast University, China · University of Thessaly, Greece
You're Pushing My Buttons: Instrumented Learning of Gentle Button Presses
Apr 7, 2026Raman Talwar, Remko Proesmans, Thomas Lips +2Contact-Rich Robotic Manipulation Imitation Learning
AI and Robotics Lab (IDLab-AIRO), Ghent University—imec, Ghent, Belgium
LatentMimic: Terrain-Adaptive Locomotion via Latent Space Imitation
Apr 7, 2026Zhiquan Wang, Yunyu Liu, Dipam Patel +3Terrain-Aware Robot Locomotion Quadruped Locomotion
Department of Computer Science, Purdue University, USA
Human-Robot Copilot for Data-Efficient Imitation Learning
Apr 4, 2026Rui Yan, Zaitian Gongye, Lars Paulsen +2Imitation Learning Robot Imitation Learning
UC San Diego
Optimizing Neurorobot Policy under Limited Demonstration Data through Preference Regret
Apr 4, 2026Viet Dung Nguyen, Yuhang Song, Anh Nguyen +3Learning from Demonstration Robot Imitation Learning
Rochester Institute of Technology · Advanced Micro Devices, Inc. · University of Liverpool
Behavior-Constrained Reinforcement Learning with Receding-Horizon Credit Assignment for High-Performance Control
Apr 3, 2026Siwei Ju, Jan Tauberschmidt, Oleg Arenz +2Constrained RL RL for Autonomous Driving
Intelligent Autonomous Systems Lab, Department of Computer Science, TU Darmstadt, Germany. · Porsche AG, Stuttgart, Germany. · German Research Center for Artificial Intelligence (DFKI), Germany.

May 12, 2026J/K move · Enter open · S save

Coordinated Diffusion: Generating Multi-Agent Behavior Without Multi-Agent Demonstrations

Lasse Peters, Laura Ferranti, Andrea Bajcsy, Javier Alonso-Mora

University of California, Berkeley · Delft University of Technology · Carnegie Mellon University

Multi-Agent Coordination Diffusion Policy Imitation Learning Multi-Robot Coordination Robot Imitation Learning

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Imitation learning powered by generative models has proven effective for modeling complex single-agent behaviors. However, teaching multi-agent systems, like multiple arms or vehicles, to coordinate through imitation learning is hindered by a fundamental data bottleneck: as the joint state-action space grows exponentially with the number of agents, collecting a sufficient amount of coordinated multi-agent demonstrations becomes extremely costly. In this work, we ask: how can we leverage single-agent demonstration data to learn multi-agent policies? We present Coordinated Diffusion (CoDi), a framework that couples independently trained single-agent diffusion policies through a user-defined multi-agent cost function, without requiring any coordinated demonstrations. We derive a new diffusion-based sampling scheme wherein the diffusion score function decomposes into independent, single-agent pre-trained base policies plus a cost-driven guidance term that coordinates these base policies into cohesive multi-agent behavior. We show that this guidance term can be estimated in a gradient-free manner, making CoDi applicable to black-box, non-differentiable cost functions without additional training. Theoretically and empirically, we analyze the conditions under which this composition can faithfully approximate a target multi-agent behavior. We find a complementary role for demonstration data versus the cost function: single-agent demonstrations must cover the support of the desired multi-agent behavior, while the cost function must promote desired behavior from this product of single-agent policies. Our results in simulation and hardware experiments of a two-arm manipulation task show that CoDi discovers robust coordinated behavior from single-agent data, is more data-efficient than multi-agent baselines, and highlights the importance of joint guidance, base policy support, and cost design.