分阶段训练让人形机器人高效自愈摔倒，真实场景表现强。
HiFAR: Multi-Stage Curriculum Learning for High-Dynamics Humanoid Fall Recovery
- 分阶段渐进式训练，从简单到复杂逐步提升恢复能力。
- 真实机器人实验成功恢复多种摔倒姿态，成功率高、反应快。
- 适合需要强鲁棒性与自适应能力的仿人机器人研究者。
人形机器人在动态非结构化环境中自主恢复摔倒面临巨大挑战。传统控制方法难以应对高维动力学与接触丰富的恢复难题，而强化学习又受限于稀疏奖励、复杂碰撞及仿真与现实间的差距。本文提出多阶段课程学习框架HiFAR，通过逐步引入更复杂、更高维度的恢复任务，帮助机器人习得高效稳定的摔倒恢复策略，并能适应真实世界中的跌倒事件。我们在真实人形机器人上评估该方法，结果表明其可自主应对多种摔倒情形，具备高成功率、快速恢复、强鲁棒性与良好泛化能力。
原文摘要 · Abstract (English)
Humanoid robots encounter considerable difficulties in autonomously recovering from falls, especially within dynamic and unstructured environments. Conventional control methodologies are often inadequate in addressing the complexities associated with high-dimensional dynamics and the contact-rich nature of fall recovery. Meanwhile, reinforcement learning techniques are hindered by issues related to sparse rewards, intricate collision scenarios, and discrepancies between simulation and real-world applications. In this study, we introduce a multi-stage curriculum learning framework, termed HiFAR. This framework employs a staged learning approach that progressively incorporates increasingly complex and high-dimensional recovery tasks, thereby facilitating the robot's acquisition of efficient and stable fall recovery strategies. Furthermore, it enables the robot to adapt its policy to effectively manage real-world fall incidents. We assess the efficacy of the proposed method using a real humanoid robot, showcasing its capability to autonomously recover from a diverse range of falls with high success rates, rapid recovery times, robustness, and generalization.
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