SCDP: Learning Humanoid Locomotion from Partial Observations via Mixed-Observation Distillation
Distilling humanoid locomotion control from offline datasets into deployable policies remains a challenge, as existing methods rely on privileged full-body states that require complex and often unreliable state estimation. We present Sensor-Conditioned Diffusion Policies (SCDP) that enables humanoid locomotion using only onboard sensors, eliminating the need for explicit state estimation. SCDP decouples sensing from supervision through mixed-observation training: diffusion model conditions on sensor histories while being supervised to predict privileged future state-action trajectories, enforcing the model to infer the motion dynamics under partial observability. We further develop restri...
| Authors | Milo Carroll, Tianhu Peng, Lingfan Bao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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