用强化学习让人形机器人跌倒后自动恢复站立，适应性强且训练快。
FRASA: An End-to-End Reinforcement Learning Agent for Fall Recovery and Stand Up of Humanoid Robots
- 基于深度强化学习统一设计跌倒恢复与起身策略
- 在Sigmaban机器人上表现优于2023年世界冠军的方案
- 训练效率高，能应对突发干扰，适合动态环境应用
人形机器人在动态环境中实现稳定行走和跌倒后恢复仍面临重大挑战。传统方法如模型预测控制（MPC）和关键帧法（KFB）或需大量调参，或缺乏实时适应性。本文提出FRASA，一种整合跌倒恢复与起身策略的端到端深度强化学习（DRL）代理。利用Cross-Q算法，FRASA显著缩短训练时间，并提供可适应不可预测干扰的通用恢复策略。在Sigmaban人形机器人上的对比测试显示，FRASA性能优于罗博杯2023年世界冠军（Rhoban团队）所采用的KFB方法。
原文摘要 · Abstract (English)
Humanoid robotics faces significant challenges in achieving stable locomotion and recovering from falls in dynamic environments. Traditional methods, such as Model Predictive Control (MPC) and Key Frame Based (KFB) routines, either require extensive fine-tuning or lack real-time adaptability. This paper introduces FRASA, a Deep Reinforcement Learning (DRL) agent that integrates fall recovery and stand up strategies into a unified framework. Leveraging the Cross-Q algorithm, FRASA significantly reduces training time and offers a versatile recovery strategy that adapts to unpredictable disturbances. Comparative tests on Sigmaban humanoid robots demonstrate FRASA superior performance against the KFB method deployed in the RoboCup 2023 by the Rhoban Team, world champion of the KidSize League.
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