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arXiv · 2507.21533

Model Predictive Adversarial Imitation Learning for Planning from Observation

Tyler Han·Yanda Bao·Bhaumik Mehta·Gabriel Guo·Anubhav Vishwakarma·Emily Kang·Sanghun Jung·Rosario Scalise·Jason Zhou·Bryan Xu·Byron Boots
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Abstract

Human demonstration data is often ambiguous and incomplete, motivating imitation learning approaches that also exhibit reliable planning behavior. A common paradigm to perform planning-from-demonstration involves learning a reward function via Inverse Reinforcement Learning (IRL) then deploying this reward via Model Predictive Control (MPC). Towards unifying these methods, we derive a replacement of the policy in IRL with a planning-based agent. With connections to Adversarial Imitation Learning, this formulation enables end-to-end interactive learning of planners from observation-only demonstrations. In addition to benefits in interpretability, complexity, and safety, we study and observe significant improvements on sample efficiency, out-of-distribution generalization, and robustness. The study includes evaluations in both simulated control benchmarks and real-world navigation experiments using few-to-single observation-only demonstrations.

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Tyler Han, Yanda Bao, Bhaumik Mehta, Gabriel Guo, Anubhav Vishwakarma, Emily Kang, Sanghun Jung, Rosario Scalise, Jason Zhou, Bryan Xu, Byron Boots. 2025-07-29. Model Predictive Adversarial Imitation Learning for Planning from Observation. https://arxiv.org/abs/2507.21533

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