X-Loco：通过协同策略蒸馏实现通用人形机器人运动控制
X-Loco: Towards Generalist Humanoid Locomotion Control via Synergetic Policy Distillation
AI总结：
提出X-Loco框架，通过协同策略蒸馏和案例自适应专家选择，训练视觉通用人形运动策略，整合直立行走、全身协调和跌倒恢复，仅基于速度指令，无需参考运动。
摘要：
尽管近期进展在单个类人技能（如直立行走、跌倒恢复和全身协调）上表现出色，但由于多样化的动力学和冲突的控制目标，学习一个掌握所有这些技能的单一策略仍然具有挑战性。为此，我们引入X-Loco，一个用于训练基于视觉的通用人形运动策略的框架。X-Loco训练多个专家策略，并采用协同策略蒸馏与案例自适应专家选择机制，动态利用多个专家策略来指导基于视觉的学生策略。这种设计使学生能够获得广泛的运动技能，从跌倒恢复到地形穿越和全身协调技能。据我们所知，X-Loco是第一个展示基于视觉的人形运动的框架，该框架联合集成了直立行走、全身协调和跌倒恢复，且仅基于速度命令运行，无需依赖参考运动。实验结果表明，X-Loco实现了卓越的性能，通过跌倒恢复和地形穿越等任务得到证明。消融研究进一步强调，我们的框架有效利用了专家知识并提高了学习效率。
英文摘要：
While recent advances have demonstrated strong performance in individual humanoid skills such as upright locomotion, fall recovery and whole-body coordination, learning a single policy that masters all these skills remains challenging due to the diverse dynamics and conflicting control objectives involved. To address this, we introduce X-Loco, a framework for training a vision-based generalist humanoid locomotion policy. X-Loco trains multiple oracle specialist policies and adopts a synergetic policy distillation with a case-adaptive specialist selection mechanism, which dynamically leverages multiple specialist policies to guide a vision-based student policy. This design enables the student to acquire a broad spectrum of locomotion skills, ranging from fall recovery to terrain traversal and whole-body coordination skills. To the best of our knowledge, X-Loco is the first framework to demonstrate vision-based humanoid locomotion that jointly integrates upright locomotion, whole-body coordination and fall recovery, while operating solely under velocity commands without relying on reference motions. Experimental results show that X-Loco achieves superior performance, demonstrated by tasks such as fall recovery and terrain traversal. Ablation studies further highlight that our framework effectively leverages specialist expertise and enhances learning efficiency.
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。
