强化学习用于腿式和轮腿式机器人的盲攀爬楼梯
Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots
AI总结：
本研究提出基于位置公式化强化学习和非对称演员-评论家结构的通用控制器，使腿式和轮腿式机器人实现盲攀爬楼梯，并成功让Ascento在现实世界攀爬15厘米楼梯。
摘要：
近年来，腿式和轮腿式机器人在主要为人类设计的环境中执行任务方面日益受到重视，这些环境遍布多个领域。这些机器人面临的重大挑战之一是其在楼梯导航方面的能力有限，这阻碍了它们在多层环境中的功能性。本研究提出了一种旨在解决这一局限的方法，利用强化学习开发一种适用于广泛机器人的通用控制器。与传统的基于速度的控制器相比，我们的方法基于位置公式化强化学习任务，我们证明这对于攀爬楼梯至关重要。此外，该方法利用非对称演员-评论家结构，能够在训练期间利用模拟环境中的特权信息，同时在实际部署中消除对外部感知传感器的依赖。所提出方法的另一个关键特征是控制器内包含一个布尔观测值，使得能够激活或停用攀爬楼梯模式。我们在不同四足和双足机器人的仿真中展示了结果，并展示了我们的方法如何使平衡机器人Ascento能够在现实世界中攀爬15厘米的楼梯，这一任务此前对该机器人而言是不可能完成的。
英文摘要：
In recent years, legged and wheeled-legged robots have gained prominence for tasks in environments predominantly created for humans across various domains. One significant challenge faced by many of these robots is their limited capability to navigate stairs, which hampers their functionality in multi-story environments. This study proposes a method aimed at addressing this limitation, employing reinforcement learning to develop a versatile controller applicable to a wide range of robots. In contrast to the conventional velocity-based controllers, our approach builds upon a position-based formulation of the RL task, which we show to be vital for stair climbing. Furthermore, the methodology leverages an asymmetric actor-critic structure, enabling the utilization of privileged information from simulated environments during training while eliminating the reliance on exteroceptive sensors during real-world deployment. Another key feature of the proposed approach is the incorporation of a boolean observation within the controller, enabling the activation or deactivation of a stair-climbing mode. We present our results on different quadrupeds and bipedal robots in simulation and showcase how our method allows the balancing robot Ascento to climb 15cm stairs in the real world, a task that was previously impossible for this robot.
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。
