用强化学习训练四足机器人自适应跌倒恢复，适配复杂地形。
Learning an Adaptive Fall Recovery Controller for Quadrupeds on Complex Terrains
- 基于深度强化学习构建自适应跌倒恢复控制器
- 在岩石、陡坡等复杂地形上成功率与恢复速度更优
- 可直接迁移至Spot、ANYmal等主流四足平台
四足机器人在复杂环境中的行走已展现潜力，但在挑战性地形上的跌倒恢复仍是重大难题。本文提出一种自适应跌倒恢复（AFR）控制器，适用于岩石、碎石、陡坡及不规则石块等复杂地形。利用深度强化学习训练AFR控制器，使其能适应广泛的地形几何形状与物理特性。我们在Isaac Gym中使用Go1进行训练，并直接迁移到Spot和ANYmal等主流四足平台。此外，在Gazebo中验证了控制器的有效性。结果表明，AFR控制器在复杂地形上具有良好的泛化能力，在恢复成功率与恢复速度上均优于基线方法。
原文摘要 · Abstract (English)
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.
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