Mind the Phase: Effective Rank and Representation Health in Legged Locomotion

The paper investigates how reinforcement‑learning policies for legged robots encode gait information by examining the effective rank of the policy Jacobian conditioned on gait phase. It finds that common architectural features such as layer normalization and residual connections allocate more representational capacity to swing than stance, a pattern absent in vanilla MLPs. Leveraging these insights, the authors propose a simple recipe that improves sim‑to‑real transfer, reducing joint jitter on a physical Spot robot by roughly three‑fold.

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• reinforcement-learning
• robotics