Learning and Retrieval from Prior Data for Skill-based Imitation Learning
Proceedings of The 6th Conference on Robot Learning, PMLR 205:2181-2204, 2023.
Imitation learning offers a promising path for robots to learn general-purpose tasks, but traditionally has enjoyed limited scalability due to high data supervision requirements and brittle generalization. Inspired by recent work on skill-based imitation learning, we investigate whether leveraging prior data from previous related tasks can enable learning novel tasks in a more robust, data-efficient manner. To make effective use of the prior data, the agent must internalize knowledge from the prior data and contextualize this knowledge in novel tasks. To that end we propose a skill-based imitation learning framework that extracts temporally-extended sensorimotor skills from prior data and subsequently learns a policy for the target task with respect to these learned skills. We find a number of modeling choices significantly improve performance on novel tasks, namely representation learning objectives to enable more predictable and consistent skill representations and a retrieval-based data augmentation procedure to increase the scope of supervision for the policy. On a number of multi-task manipulation domains, we demonstrate that our method significantly outperforms existing imitation learning and offline reinforcement learning approaches. Videos and code are available at https://ut-austin-rpl.github.io/sailor