学习在复杂地形上四足机器人的自适应跌倒恢复控制器
Learning an Adaptive Fall Recovery Controller for Quadrupeds on Complex Terrains
AI总结：
本文提出一种基于深度强化学习的自适应跌倒恢复（AFR）控制器，使四足机器人能在岩石、陡坡等复杂地形上有效恢复，并在Isaac Gym中训练后成功迁移至Spot和ANYmal等平台，泛化性、成功率和恢复速度均优于基线方法。
摘要：
腿式机器人在复杂环境中的运动展现出潜力，但在具有挑战性的地形上从跌倒中恢复仍然是一个重大障碍。本文提出了一种用于四足机器人在岩石、溪流、陡坡和不规则石头等具有挑战性的地形上的自适应跌倒恢复（AFR）控制器。我们利用深度强化学习训练AFR，使其能够适应各种地形几何形状和物理特性。我们的方法相比现有方法有所改进，在具有挑战性的地形上的恢复场景中展现出有希望的结果。我们在Isaac Gym中使用Go1训练了我们的方法，并直接将其转移到几个主流的四足平台上，如Spot和ANYmal。此外，我们在Gazebo中验证了控制器的有效性。结果表明，AFR控制器能够很好地泛化到复杂地形，并在成功率和恢复速度方面优于基线方法。
英文摘要：
Legged robots have shown promise in locomotion complex environments, but recovery from falls on challenging terrains remains a significant hurdle. This paper presents an Adaptive Fall Recovery (AFR) controller for quadrupedal robots on challenging terrains such as rocky, breams, steep slopes, and irregular stones. We leverage deep reinforcement learning to train the AFR, which can adapt to a wide range of terrain geometries and physical properties. Our method demonstrates improvements over existing approaches, showing promising results in recovery scenarios on challenging terrains. We trained our method in Isaac Gym using the Go1 and directly transferred it to several mainstream quadrupedal platforms, such as Spot and ANYmal. Additionally, we validated the controller's effectiveness in Gazebo. Our results indicate that the AFR controller generalizes well to complex terrains and outperforms baseline methods in terms of success rate and recovery speed.
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。
