Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization

TL;DR

This paper introduces an adversarial regularization method for chaining skills in long-horizon robot manipulation tasks, enabling successful policy composition without requiring extensive initial state coverage.

Contribution

It proposes a novel terminal state regularization technique within an adversarial learning framework to improve skill chaining for complex manipulation tasks.

Findings

First model-free RL algorithm to solve furniture assembly tasks

Outperforms prior skill chaining approaches on long-horizon tasks

Achieves successful long-horizon manipulation without large initial state distributions

Abstract

Skill chaining is a promising approach for synthesizing complex behaviors by sequentially combining previously learned skills. Yet, a naive composition of skills fails when a policy encounters a starting state never seen during its training. For successful skill chaining, prior approaches attempt to widen the policy's starting state distribution. However, these approaches require larger state distributions to be covered as more policies are sequenced, and thus are limited to short skill sequences. In this paper, we propose to chain multiple policies without excessively large initial state distributions by regularizing the terminal state distributions in an adversarial learning framework. We evaluate our approach on two complex long-horizon manipulation tasks of furniture assembly. Our results have shown that our method establishes the first model-free reinforcement learning algorithm to solve these tasks; whereas prior skill chaining approaches fail. The code and videos are available at https://clvrai.com/skill-chaining