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.

翻译：人形机器人在动态非结构化环境中自主实现跌倒恢复面临显著挑战。传统控制方法通常难以应对高维动力学特性及密集接触交互带来的复杂性。同时，强化学习方法受限于稀疏奖励机制、复杂碰撞场景以及仿真与现实应用间的差异。本研究提出一种名为HiFAR的多阶段课程学习框架，该框架采用分阶段学习策略，逐步引入复杂度递增的高维恢复任务，从而促进机器人习得高效稳定的跌倒恢复策略。此外，该框架使机器人能够调整其控制策略以有效应对真实世界的跌倒场景。我们通过真实人形机器人平台验证了所提方法的有效性，结果表明该方法能实现高成功率、快速恢复时间、强鲁棒性和良好泛化能力的多样化跌倒自主恢复。