Mind the Phase: Effective Rank and Representation Health in Legged Locomotion
Reinforcement learning has become the leading paradigm in legged locomotion, enabling complex behaviors from backflips to parkour through massively parallel simulation. Under PPO's non-stationarity, shallow networks remain the de facto architecture, supported by carefully staged curricula and environments, yet the representations these policies learn stay poorly understood, leaving no training-time signal of how they will behave on hardware. In this work, we empirically study locomotion policies through the effective rank of the policy Jacobian and show that conditioning rank on the gait phase exposes architectural structure that global rank averages away. In particular, we find that stan...
| Authors | Felipe Tommaselli, Thiago H. Segreto, Juliano D. Negri et al. |
|---|---|
| Primary category | cs.RO |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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