基于条件对抗运动先验的多技能腿部运动学习
Learning Multi-Skill Legged Locomotion Using Conditional Adversarial Motion Priors
AI总结：
提出基于条件对抗运动先验的多技能学习框架，使四足机器人从专家演示中高效获取多样化运动技能，并通过技能判别器和条件奖励实现精确重建与平滑切换。
摘要：
尽管开发模仿生物运动以在复杂环境中敏捷导航的腿式机器人日益受到关注，但获取多样化的技能库仍然是机器人学中的一个基本挑战。现有方法可以从专家数据中学习运动行为，但它们通常无法通过单一策略获取多种运动技能，并且缺乏平滑的技能转换。我们提出了一种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够从专家演示中高效地获取多样化的运动技能。通过新颖的技能判别器和技能条件奖励设计，实现了精确的技能重建。该框架整体支持多种技能的主动控制和重用，为在复杂环境中学习可泛化策略提供了一种实用解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
