基于学习地形想象的四足机器人鲁棒恢复运动控制
Robust Recovery Motion Control for Quadrupedal Robots via Learned Terrain Imagination
AI总结：
针对四足机器人在杂乱环境中易摔倒且现有恢复控制器适应性差的问题，提出基于学习地形想象的鲁棒越野恢复策略，经仿真和真实测试验证其优越性。
摘要：
四足机器人已成为协助人类的尖端平台，在偏远地区的检查和探索任务中得到应用。然而，其浮动基座结构使其在杂乱环境中容易摔倒，而人工操作员可能无法随时进行手动恢复。最近几项研究提出了采用深度强化学习算法的恢复控制器。然而，这些控制器并非专门设计用于在杂乱环境（如楼梯和斜坡）中有效运行，这限制了它们的适用性。在本研究中，我们提出了一种鲁棒的越野恢复策略，以促进在杂乱环境中的快速和安全恢复。我们通过涵盖多种地形类型的仿真和真实世界测试证实了我们所提出方法的优越性。
英文摘要：
Quadrupedal robots have emerged as a cutting-edge platform for assisting humans, finding applications in tasks related to inspection and exploration in remote areas. Nevertheless, their floating base structure renders them susceptible to fall in cluttered environments, where manual recovery by a human operator may not always be feasible. Several recent studies have presented recovery controllers employing deep reinforcement learning algorithms. However, these controllers are not specifically designed to operate effectively in cluttered environments, such as stairs and slopes, which restricts their applicability. In this study, we propose a robust all-terrain recovery policy to facilitate rapid and secure recovery in cluttered environments. We substantiate the superiority of our proposed approach through simulations and real-world tests encompassing various terrain types.
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。
