基于多智能体强化学习的协作长绳跳绳
Cooperative Long Rope Skipping via Multi-Agent Reinforcement Learning
AI总结：
提出Marope框架，采用分层强化学习实现多个人形机器人的协作长绳跳绳，通过多智能体强化学习训练分散的摇绳策略，上层调度策略协调执行，并融入多样跳跃策略提升泛化能力，在仿真和真实实验中优于基线方法。
摘要：
人类展现出卓越的运动敏捷性，能够完成跑步、跳跃等多种动态技能，这凸显了人形机器人在运动方面的巨大潜力。在竞技体育中，长绳跳绳需要两名摇绳者协同摇绳，同时适应不同跳跃节奏的玩家，这对人形机器人来说是一项有意义但具有挑战性的任务。尽管现有的人形机器人运动方法在单智能体和无交互场景（如跑步、舞蹈和跑酷）中取得了成功，但需要多参与者精确协调的任务场景仍鲜有探索。为此，我们提出Marope，一个用于多个人形机器人协作长绳跳绳的多智能体强化学习框架。具体而言，Marope采用分层强化学习框架进行策略训练。在底层，通过多智能体强化学习学习分散的摇绳操作策略；在顶层，训练集中调度策略以协调底层策略的执行。为了提高对不同玩家行为风格的泛化能力，Marope进一步将多样化的跳跃策略融入协作博弈训练中。我们在仿真和真实环境中对宇树G1人形机器人进行了评估。实验结果表明，Marope优于多种基线方法，实现了更高效稳定的摇绳操作以及与不同玩家更鲁棒和自适应的协作。
英文摘要：
Humans exhibit remarkable motor agility, enabling a wide range of dynamic skills such as running and jumping, which highlights the great potential of humanoid robots for athletic locomotion. Among athletic sports, long rope skipping requires two rope turners to cooperatively swing the rope while adapting to a player under different jumping rhythms, making it a meaningful yet challenging task for humanoid robots. Although existing methods for humanoid sports have achieved success in single-agent and interaction-free settings, such as running, dancing, and parkour, task scenarios that require precise coordination among multiple participants remain largely unexplored. To this end, we propose Marope, a multi-agent reinforcement learning (MARL) framework for cooperative long rope skipping with multiple humanoid robots. Specifically, Marope adopts a hierarchical reinforcement learning framework for policy training. At the lower level, it learns decentralized rope manipulation policies through MARL, while at the upper level, a centralized scheduling policy is trained to coordinate the execution of the lower-level policies. To improve generalization across different player behavioral styles, Marope further incorporates diverse jumping policies into cooperative game training. We evaluate our approach on Unitree G1 humanoid robots in both simulation and real-world settings. Experimental results demonstrate that Marope outperforms various baselines, achieving more efficient and stable rope manipulation as well as more robust and adaptable cooperation with varied players.
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。
