mlsanityData to 14 Oct 2026

Structured Imitation Learning of Interactive Policies through Inverse Games

arXiv 2511.12848 ↗First posted 17 Nov 2025cs.RO cs.LGcited by 0

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

Generative model-based imitation learning methods have recently achieved strong results in learning high-complexity motor skills from human demonstrations. However, imitation learning of interactive policies that coordinate with humans in shared spaces without explicit communication remains challenging, due to the significantly higher behavioral complexity in multi-agent interactions compared to non-interactive tasks. In this work, we introduce a structured imitation learning framework for interactive policies by combining generative single-agent policy learning with a flexible yet expressive game-theoretic structure. Our method explicitly separates learning into two steps: first, we learn individual behavioral patterns from multi-agent demonstrations using standard imitation learning; then, we structurally learn inter-agent dependencies by solving an inverse game problem. Preliminary results in a synthetic 5-agent social navigation task show that our method significantly improves non-interactive policies and performs comparably to the ground truth interactive policy using only 50 demonstrations. These results highlight the potential of structured imitation learning in interactive settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 13 of its 19 references that are in the map. First posted on arXiv 17 Nov 2025.

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13 of 19 references.

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Same direction, newest first

  1. 2024BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay22 Feb

Other references · 12

  1. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,838
  2. 2020Learning Agile Robotic Locomotion Skills by Imitating Animalscited by 251
  3. 2024HumanPlus: Humanoid Shadowing and Imitation from Humanscited by 185
  4. 2024Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matchingcited by 65
  5. 2018Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddingscited by 52
  6. 2019Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Gamescited by 37
  7. 2023Learning Human-to-Robot Handovers from Point Cloudscited by 34
  8. 2023Aligning Robot and Human Representationscited by 11
  9. 2022Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrationscited by 10
  10. 2024Mixed Strategy Nash Equilibrium for Crowd Navigationcited by 3
  11. 2025Inverse Mixed Strategy Games with Generative Trajectory Modelscited by 3
  12. 2025Autonomous Human-Robot Interaction via Operator Imitationcited by 3

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  2. 2026Coordinated Diffusion: Generating Multi-Agent Behavior Without Multi-Agent Demonstrations
  3. 2022Diverse Imitation Learning via Self-Organizing Generative Models
  4. 2023Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games
  5. 2022A Ranking Game for Imitation Learning
  6. 2024Reward-free World Models for Online Imitation Learning
  7. 2021Imitation Learning: Progress, Taxonomies and Challenges
  8. 2023MimicPlay: Long-Horizon Imitation Learning by Watching Human Play