Integrating Controllable Motion Skills from Demonstrations

TL;DR

This paper presents CSI, a flexible framework for integrating diverse legged robot motion skills into a single policy without complex reward tuning, and enables language-directed control through hierarchical coupling.

Contribution

The introduction of CSI allows multi-skill integration without reward engineering and supports language-based skill control, enhancing flexibility and scalability.

Findings

CSI effectively integrates multiple diverse skills.

Smooth transitions between different skills are achieved.

Framework scales well with increasing number of skills.

Abstract

The expanding applications of legged robots require their mastery of versatile motion skills. Correspondingly, researchers must address the challenge of integrating multiple diverse motion skills into controllers. While existing reinforcement learning (RL)-based approaches have achieved notable success in multi-skill integration for legged robots, these methods often require intricate reward engineering or are restricted to integrating a predefined set of motion skills constrained by specific task objectives, resulting in limited flexibility. In this work, we introduce a flexible multi-skill integration framework named Controllable Skills Integration (CSI). CSI enables the integration of a diverse set of motion skills with varying styles into a single policy without the need for complex reward tuning. Furthermore, in a hierarchical control manner, the trained low-level policy can be coupled with a high-level Natural Language Inference (NLI) module to enable preliminary language-directed skill control. Our experiments demonstrate that CSI can flexibly integrate a diverse array of motion skills more comprehensively and facilitate the transitions between different skills. Additionally, CSI exhibits good scalability as the number of motion skills to be integrated increases significantly.