在仿人机器人中利用自适应辅助课程力学习运动技能
Learning Motion Skills with Adaptive Assistive Curriculum Force in Humanoid Robots
AI总结：
受人类学习技能依赖外部支持的启发，提出A2CF框架，通过双智能体系统施加自适应辅助课程力引导仿人机器人学习，显著加快收敛速度并降低失败率，最终获得无需支持的稳健策略。
摘要：
为复杂仿人机器人任务学习策略既具挑战性又极具吸引力。受婴儿和运动员依赖外部支持（如学步车或教练施加的引导）来掌握行走、舞蹈和空翻等技能的启发，我们提出了A2CF：用于仿人机器人运动学习的自适应辅助课程力。A2CF训练一个双智能体系统，其中一个专门的辅助力智能体施加依赖于状态的力，引导机器人完成困难的初始动作，并随着机器人熟练度的提高逐渐减少辅助。在三个基准测试——双足行走、编排舞蹈和后空翻中，A2CF的收敛速度比基线方法快30%，失败率降低超过40%，最终产生稳健的、无需支持的策略。真实世界实验进一步证明，自适应施加的辅助力显著加速了高维机器人控制中复杂技能的获取。
英文摘要：
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.
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。
