Reproduce “Adapting Rapid Motor Adaptation for Bipedal Robots” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Ashish Kumar, Zhongyu Li, Jun Zeng, Deepak Pathak, Koushil Sreenath, Jitendra Malik
Researchers at the University of California Berkeley and Carnegie Mellon University developed A-RMA, an extension of the Rapid Motor Adaptation framework, specifically designed to enhance robust locomotion for bipedal robots like Cassie. This method mitigates performance degradation caused by imperfect online environmental parameter estimation, achieving stable and adaptive walking on various challenging terrains in zero-shot real-world deployment.
Key insights
- The primary cause of performance degradation in adaptive policies like RMA on bipedal robots is the discrepancy between perfect extrinsics used during initial policy training and the imperfect, noisy extrinsics estimated online during deployment.
- Conditioning the base policy on these imperfectly estimated extrinsics during a specific finetuning phase allows the policy to account for real-world estimation inaccuracies and recover significant performance.
- Combining gait imitation for warm-starting with domain randomization and a dedicated adaptation module enables robust and generalized locomotion for complex bipedal systems.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.