Adapting Rapid Motor Adaptation for Bipedal Robots
This work addresses the problem of bipedal robot locomotion for robotics applications, representing an incremental improvement by adapting existing methods to a more complex domain.
The paper tackles the challenge of designing walking controllers for inherently unstable bipedal robots by extending rapid adaptation methods, proposing A-RMA to adapt the base policy for imperfect environment estimators, and demonstrates that it outperforms baseline controllers in simulation and enables zero-shot real-world deployment on the Cassie robot in diverse scenarios.
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/