让四足机器人学会多种运动技能并流畅切换
Learning Multi-Skill Legged Locomotion Using Conditional Adversarial Motion Priors
- 用条件对抗运动先验学习多技能，支持技能条件化控制
- 通过新设计的技能判别器和奖励机制实现精准技能重建
- 适合需要灵活适应复杂环境的机器人系统研究者
尽管人们对开发能模拟生物运动以在复杂环境中敏捷导航的四足机器人兴趣日益增长，但获取多样化的运动技能仍是机器人领域的一个根本挑战。现有方法虽可从专家数据中学习运动行为，但通常无法通过单一策略掌握多种运动技能，且缺乏平滑的技能过渡。本文提出基于条件对抗运动先验（CAMP）的多技能学习框架，旨在使四足机器人能高效地从专家示范中习得多样化的运动技能。通过新型技能判别器与技能条件化奖励设计，实现了精确的技能重建。该框架支持多技能的主动控制与复用，为复杂环境中学习通用策略提供了实用解决方案。
原文摘要 · Abstract (English)
Despite growing interest in developing legged robots that emulate biological locomotion for agile navigation of complex environments, acquiring a diverse repertoire of skills remains a fundamental challenge in robotics. Existing methods can learn motion behaviors from expert data, but they often fail to acquire multiple locomotion skills through a single policy and lack smooth skill transitions. We propose a multi-skill learning framework based on Conditional Adversarial Motion Priors (CAMP), with the aim of enabling quadruped robots to efficiently acquire a diverse set of locomotion skills from expert demonstrations. Precise skill reconstruction is achieved through a novel skill discriminator and skill-conditioned reward design. The overall framework supports the active control and reuse of multiple skills, providing a practical solution for learning generalizable policies in complex environments.
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