论文标题
调整双皮亚机器人的快速运动改编
Adapting Rapid Motor Adaptation for Bipedal Robots
论文作者
论文摘要
腿部运动的最新进展使四足动物能够在具有挑战性的地形上行走。但是，两足动物本质上更加不稳定，因此很难为其设计步行控制器。在这项工作中，我们利用了快速适应机车控制的最新进展，并将其扩展到在两足机器人上的工作。与现有作品类似，我们从基本策略开始，该策略从适应模块中作为输入估计的外部向量进行了动作。该外部媒介包含有关环境的信息，并使步行控制器能够快速在线适应。但是，外部估计器可能是不完善的，这可能导致基本政策的性能不佳，这预计是一个完美的估计器。在本文中，我们提出了A-RMA（适应RMA），该A-RMA（适应RMA）还通过使用无模型的RL对其进行鉴定，从而适应了不完美的外部外部估计器的基本策略。我们证明，A-RMA在仿真中胜过许多基于RL的基线控制器和基于模型的控制器，并显示了单个A-RMA策略的零拍摄部署，以使Cassie能够在现实世界中各种不同的场景中行走。 https://ashish-kmr.github.io/a-rma/的视频和结果
Recent advances in legged locomotion have enabled quadrupeds to walk on challenging terrains. However, bipedal robots are inherently more unstable and hence it's harder to design walking controllers for them. In this work, we leverage recent advances in rapid adaptation for locomotion control, and extend them to work on bipedal robots. Similar to existing works, we start with a base policy which produces actions while taking as input an estimated extrinsics vector from an adaptation module. This extrinsics vector contains information about the environment and enables the walking controller to rapidly adapt online. However, the extrinsics estimator could be imperfect, which might lead to poor performance of the base policy which expects a perfect estimator. In this paper, we propose A-RMA (Adapting RMA), which additionally adapts the base policy for the imperfect extrinsics estimator by finetuning it using model-free RL. We demonstrate that A-RMA outperforms a number of RL-based baseline controllers and model-based controllers in simulation, and show zero-shot deployment of a single A-RMA policy to enable a bipedal robot, Cassie, to walk in a variety of different scenarios in the real world beyond what it has seen during training. Videos and results at https://ashish-kmr.github.io/a-rma/
