Watch and Match: Supercharging Imitation with Regularized Optimal TransportDownload PDF

Published: 10 Sept 2022, Last Modified: 12 Mar 2024CoRL 2022 OralReaders: Everyone
Keywords: Imitation Learning, Manipulation, Robotics
TL;DR: We propose a new imitation learning algorithm that substantially improves sample efficiency for continuous control problems in simulation and on real-world robotic manipulation tasks.
Abstract: Imitation learning holds tremendous promise in learning policies efficiently for complex decision making problems. Current state-of-the-art algorithms often use inverse reinforcement learning (IRL), where given a set of expert demonstrations, an agent alternatively infers a reward function and the associated optimal policy. However, such IRL approaches often require substantial online interactions for complex control problems. In this work, we present Regularized Optimal Transport (ROT), a new imitation learning algorithm that builds on recent advances in optimal transport based trajectory-matching. Our key technical insight is that adaptively combining trajectory-matching rewards with behavior cloning can significantly accelerate imitation even with only a few demonstrations. Our experiments on 20 visual control tasks across the DeepMind Control Suite, the OpenAI Robotics Suite, and the Meta-World Benchmark demonstrate an average of 7.8x faster imitation to reach 90% of expert performance compared to prior state-of-the-art methods. On real-world robotic manipulation, with just one demonstration and an hour of online training, ROT achieves an average success rate of 90.1% across 14 tasks.
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