Paper
SCDP: Learning Humanoid Locomotion from Partial Observations via Mixed-Observation Distillation
Authors
Milo Carroll, Tianhu Peng, Lingfan Bao, Chengxu Zhou, Zhibin Li
Abstract
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 restricted denoising, context distribution alignment, and context-aware attention masking to encourage implicit state estimation within the model and to prevent train-deploy mismatch. We validate SCDP on velocity-commanded locomotion and motion reference tracking tasks. In simulation, SCDP achieves near-perfect success on velocity control (99-100%) and 93% tracking success in AMASS test set, performing comparable to privileged baselines while using only onboard sensors. Finally, we deploy the trained policy on a real G1 humanoid at 50 Hz, demonstrating robust real robot locomotion without external sensing or state estimation.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.09574v1</id>\n <title>SCDP: Learning Humanoid Locomotion from Partial Observations via Mixed-Observation Distillation</title>\n <updated>2026-03-10T12:21:11Z</updated>\n <link href='https://arxiv.org/abs/2603.09574v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.09574v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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 restricted denoising, context distribution alignment, and context-aware attention masking to encourage implicit state estimation within the model and to prevent train-deploy mismatch. We validate SCDP on velocity-commanded locomotion and motion reference tracking tasks. In simulation, SCDP achieves near-perfect success on velocity control (99-100%) and 93% tracking success in AMASS test set, performing comparable to privileged baselines while using only onboard sensors. Finally, we deploy the trained policy on a real G1 humanoid at 50 Hz, demonstrating robust real robot locomotion without external sensing or state estimation.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.RO'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <published>2026-03-10T12:21:11Z</published>\n <arxiv:comment>6 pages, 8 figures, 5 tables, iRos</arxiv:comment>\n <arxiv:primary_category term='cs.RO'/>\n <author>\n <name>Milo Carroll</name>\n </author>\n <author>\n <name>Tianhu Peng</name>\n </author>\n <author>\n <name>Lingfan Bao</name>\n </author>\n <author>\n <name>Chengxu Zhou</name>\n </author>\n <author>\n <name>Zhibin Li</name>\n </author>\n </entry>"
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