Nolan Fey

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.