用可自适应的辅助力加速人形机器人学复杂动作。
Learning Motion Skills with Adaptive Assistive Curriculum Force in Humanoid Robots
- 引入双智能体系统，动态施加辅助力引导学习。
- 比基线方法快30%收敛，失败率降40%以上。
- 适合需要快速掌握复杂运动技能的研究者。
复杂人形机器人任务的策略学习仍具挑战性。受婴儿和运动员依赖外部支持（如学步车或教练指导）的启发，我们提出自适应辅助课程力（A2CF），用于人形机器人运动学习。A2CF训练一个双智能体系统，其中专用辅助力智能体根据状态施加动态力，引导机器人完成初期困难动作，并随能力提升逐步减少辅助。在三类基准任务——双足行走、编排舞蹈和后空翻中，A2CF实现30%更快的收敛速度，失败率降低超过40%，最终生成无需外部支持的鲁棒策略。真实世界实验进一步验证，自适应辅助力显著加速了高维机器人控制中复杂技能的学习。
原文摘要 · Abstract (English)
Learning policies for complex humanoid tasks remains both challenging and compelling. Inspired by how infants and athletes rely on external support--such as parental walkers or coach-applied guidance--to acquire skills like walking, dancing, and performing acrobatic flips, we propose A2CF: Adaptive Assistive Curriculum Force for humanoid motion learning. A2CF trains a dual-agent system, in which a dedicated assistive force agent applies state-dependent forces to guide the robot through difficult initial motions and gradually reduces assistance as the robot's proficiency improves. Across three benchmarks--bipedal walking, choreographed dancing, and backflip--A2CF achieves convergence 30% faster than baseline methods, lowers failure rates by over 40%, and ultimately produces robust, support-free policies. Real-world experiments further demonstrate that adaptively applied assistive forces significantly accelerate the acquisition of complex skills in high-dimensional robotic control.
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