KiRAS: 基于关键帧的自我模仿学习用于四足机器人鲁棒且适应性强的技能学习
KiRAS: Keyframe Guided Self-Imitation for Robust and Adaptive Skill Learning in Quadruped Robots
AI总结：
KiRAS通过关键帧引导的自我模仿学习，在复杂地形上实现多样技能的获取与过渡，无需专家数据集，提升机器人在无结构环境中的鲁棒性和适应性。
摘要：
随着强化学习和模仿学习的进步，四足机器人可通过模仿多个技能特定数据集在单一策略中获取多样技能。然而，复杂地形的数据集缺乏限制了多技能策略在无结构环境中的泛化能力。受动画启发，我们采用关键帧作为最小和通用的技能表示，放宽数据集约束，并整合地形适应性与技能多样性。我们提出了KiRAS，一种端到端框架，用于在复杂地形上获取和过渡于多样技能原语。KiRAS首先通过关键帧引导的自我模仿学习在平坦地形上学习多样技能，无需专家数据集；然后继续在粗糙地形上训练相同策略网络以增强鲁棒性。为消除灾难性遗忘，引入基于熟练度的技能初始化技术。在Solo-8和Unitree Go1机器人上的实验表明，KiRAS实现了在挑战性地形上的鲁棒技能获取和流畅过渡。该框架展示了其作为轻量级多技能生成和数据集收集平台的潜力，并进一步实现了灵活的技能过渡，增强了在挑战性地形上的运动能力。
英文摘要：
With advances in reinforcement learning and imitation learning, quadruped robots can acquire diverse skills within a single policy by imitating multiple skill-specific datasets. However, the lack of datasets on complex terrains limits the ability of such multi-skill policies to generalize effectively in unstructured environments. Inspired by animation, we adopt keyframes as minimal and universal skill representations, relaxing dataset constraints and enabling the integration of terrain adaptability with skill diversity. We propose Keyframe Guided Self-Imitation for Robust and Adaptive Skill Learning (KiRAS), an end-to-end framework for acquiring and transitioning between diverse skill primitives on complex terrains. KiRAS first learns diverse skills on flat terrain through keyframe-guided self-imitation, eliminating the need for expert datasets; then continues training the same policy network on rough terrains to enhance robustness. To eliminate catastrophic forgetting, a proficiency-based Skill Initialization Technique is introduced. Experiments on Solo-8 and Unitree Go1 robots show that KiRAS enables robust skill acquisition and smooth transitions across challenging terrains. This framework demonstrates its potential as a lightweight platform for multi-skill generation and dataset collection. It further enables flexible skill transitions that enhance locomotion on challenging terrains.
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。
