EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data

TL;DR

EXTRACT introduces a method that leverages pre-trained vision-language models to automatically extract meaningful, transferable skills from offline data, enabling robots to learn new tasks more efficiently without human supervision.

Contribution

The paper presents a novel skill extraction approach using vision-language models that improves transferability and sample efficiency in robot learning from offline data.

Findings

EXTRACT outperforms prior skill-based RL methods in sample efficiency.

It enables faster learning of new tasks in sparse-reward, image-based environments.

The approach demonstrates significant performance gains over existing methods.

Abstract

Most reinforcement learning (RL) methods focus on learning optimal policies over low-level action spaces. While these methods can perform well in their training environments, they lack the flexibility to transfer to new tasks. Instead, RL agents that can act over useful, temporally extended skills rather than low-level actions can learn new tasks more easily. Prior work in skill-based RL either requires expert supervision to define useful skills, which is hard to scale, or learns a skill-space from offline data with heuristics that limit the adaptability of the skills, making them difficult to transfer during downstream RL. Our approach, EXTRACT, instead utilizes pre-trained vision language models to extract a discrete set of semantically meaningful skills from offline data, each of which is parameterized by continuous arguments, without human supervision. This skill parameterization allows robots to learn new tasks by only needing to learn when to select a specific skill and how to modify its arguments for the specific task. We demonstrate through experiments in sparse-reward, image-based, robot manipulation environments that EXTRACT can more quickly learn new tasks than prior works, with major gains in sample efficiency and performance over prior skill-based RL. Website at https://www.jessezhang.net/projects/extract/.