Reproduce “Embedding Classical Balance Control Principles in Reinforcement Learning for Humanoid Recovery” on OpenResearch

About this paper

Nehar Poddar, Stephen McCrory, Luigi Penco, Geoffrey Clark, Hakki Erhan Svil, Robert Griffin

A framework for humanoid robot fall recovery embeds classical balance control principles, such as capture point and CoM dynamics, directly into a reinforcement learning architecture. This approach trains a single policy that achieves 93.4% recovery success from diverse configurations in simulation and demonstrates zero-shot transfer for various recovery behaviors on the Unitree H1-2 hardware.

Key insights

• Explicitly embedding classical balance metrics, such as capture point and CoM dynamics, into the RL reward function and critic's inputs is crucial for efficiently learning robust and generalizable humanoid recovery.
• A single reinforcement learning policy can effectively learn the entire spectrum of fall recovery behaviors, including multi-contact stand-up, when trained with a structured curriculum and balance-aware signals.
• The balance-informed architecture is essential for the policy to discover and execute complex multi-contact stand-up maneuvers, rather than simply being an incidental learning aid.

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