# Humanoid Control

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arXiv Nov 9, 2026
Humanoids stop waiting for the big model: DASH holds 20 Hz from 0.8B to 8B backbones
In humanoid manipulation, a large vision-language-action backbone is slow, so synchronous execution acts on stale observations and control frequency falls as the backbone grows. DASH splits the policy into a slow vision-language model and a fast flow-matching action head running as three concurrent processes over LCM, so the control rate is set by the action head alone. On a RoboTwin benchmark that charges inference latency in simulated time, DASH holds 20 Hz from 0.8B to 8B backbones while the synchronous rate falls from 9.7 Hz to 4.1 Hz, with higher success at every size; on a real dexterous humanoid, pick-and-place succeeds in 23 of 24 trials versus 16 of 24 for the synchronous pipeline.
In humanoid manipulation, a large vision-language-action backbone is slow, so synchronous execution acts on stale observations and control frequency falls as the backbone grows. DASH splits the policy into a slow vision-language model and a fast flow-matching action head running as three concurrent processes over LCM, so the control rate is set by the action head alone. On a RoboTwin benchmark that charges inference latency in simulated time, DASH holds 20 Hz from 0.8B to 8B backbones while the synchronous rate falls from 9.7 Hz to 4.1 Hz, with higher success at every size; on a real dexterous humanoid, pick-and-place succeeds in 23 of 24 trials versus 16 of 24 for the synchronous pipeline.
Bingzhang Wang（First author）
DASH Vision-Language-Action Models Humanoid Control Flow Matching Action Chunking
arXiv Oct 14, 2026
Humanoid running and jumping need more than motion: Contact-UAN patches the actuator force model with 6.5 minutes of walking data
Humanoid policies learned in simulation can transfer motions to hardware while failing to transfer the force interactions learned in training, which costs torque efficiency, force-tracking accuracy and impact absorption. Contact-UAN keeps the simulator's rigid-body and contact physics and learns only a residual correction on the actuator command channel, using onboard proprioception to infer a stance-foot pose so replay starts with hardware-consistent contact forces. On a Unitree G1, a single residual model trained on 6.5 minutes of real walking data yields 20% lower running torque and 43% lower force-tracking error, and supports stable jumping and landing.
Humanoid policies learned in simulation can transfer motions to hardware while failing to transfer the force interactions learned in training, which costs torque efficiency, force-tracking accuracy and impact absorption. Contact-UAN keeps the simulator's rigid-body and contact physics and learns only a residual correction on the actuator command channel, using onboard proprioception to infer a stance-foot pose so replay starts with hardware-consistent contact forces. On a Unitree G1, a single residual model trained on 6.5 minutes of real walking data yields 20% lower running torque and 43% lower force-tracking error, and supports stable jumping and landing.
Massachusetts Institute of Technology
Nolan Fey（First author）
Contact-UAN Residual World Model Sim-to-Real Transfer Humanoid Control

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Humanoids stop waiting for the big model: DASH holds 20 Hz from 0.8B to 8B backbones

Humanoid running and jumping need more than motion: Contact-UAN patches the actuator force model with 6.5 minutes of walking data