追逐自主性：动态重定向和控制引导的强化学习用于高效且可控的人形跑步
Chasing Autonomy: Dynamic Retargeting and Control Guided RL for Performant and Controllable Humanoid Running
AI总结：
本文提出动态重定向方法，通过优化流程生成改进的周期参考库，结合控制引导奖励提升人形跑步的性能和可控性，实验证明在硬件上实现了高效和持久的跑步表现。
摘要：
人形机器人具有像人类一样移动的潜力，包括快速动态的跑步。最近，能够模仿人类动作的强化学习（RL）控制器变得流行，因为它们可以生成非常动态的行为，但通常局限于单一动作回放，限制了其在长时间和自主移动中的应用。本文提出了一种管道，通过带有硬约束的优化流程动态重定向人类动作，从单个人类示范中生成改进的周期参考库。然后研究了参考动作和奖励结构对参考和命令速度跟踪的影响，得出结论：目标条件和控制引导的奖励，能够跟踪动态优化的人类数据，从而获得最佳性能。我们将策略部署在硬件上，通过在Unitree G1机器人上实现最高3.3 m/s的跑步速度和在现实环境中穿越数百米，证明了其速度和耐力。此外，为了展示运动的可控性，我们在户外运行中使用控制器，结合完整的感知和规划自主栈进行障碍物避让。
英文摘要：
Humanoid robots have the promise of locomoting like humans, including fast and dynamic running. Recently, reinforcement learning (RL) controllers that can mimic human motions have become popular as they can generate very dynamic behaviors, but they are often restricted to single motion play-back which hinders their deployment in long duration and autonomous locomotion. In this paper, we present a pipeline to dynamically retarget human motions through an optimization routine with hard constraints to generate improved periodic reference libraries from a single human demonstration. We then study the effect of both the reference motion and the reward structure on the reference and commanded velocity tracking, concluding that a goal-conditioned and control-guided reward which tracks dynamically optimized human data results in the best performance. We deploy the policy on hardware, demonstrating its speed and endurance by achieving running speeds of up to 3.3 m/s on a Unitree G1 robot and traversing hundreds of meters in real-world environments. Additionally, to demonstrate the controllability of the locomotion, we use the controller in a full perception and planning autonomy stack for obstacle avoidance while running outdoors.
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。
