Skill Composition for Legged Robot Reinforcement Learning

2026-09-13

AI Summary

研究针对腿式机器人在技能转换时的不稳定性问题，提出将独立子策略的组合视为一个独立的研究问题，以实现控制权安全转移。

Abstract

Robots, and humanoid robots in particular, are increasingly competent at individual behaviors, each obtained by training a specialized controller. A specialized skill is quick to train, converges reliably because the problem it faces is narrow, and can be validated on its own, none of which is true of a single end-to-end policy asked to cover everything. What remains fragile is the transition between them. We argue that the composition of independent sub-policies deserves to be treated as a research problem in its own right, rather than as an implementation detail left to whatever mechanism happens to be at hand. Reliable composition is what turns a collection of separate skills into a repertoire that can be used, extended and shared. More fundamentally, if control can be passed between specialized policies safely, and at any moment, the choice of what the robot should do next can be delegated to a component of an entirely different nature, such as a planner, an automaton or a symbolic controller, whose behavior can be inspected in advance. The policies would then only ever have to act, and what the robot can be trusted to do would become verifiable.

Problem

• Legged Robot
• Reinforcement Learning
• Skill Composition
• Sub-policies Transition

Innovation

• Skill Composition
• Reinforcement Learning
• Legged Robots