Differentiable simulation enables torque-sensor-free actuator model identification from trajectory data, achieving 1.88x better position tracking than a stand-trained baseline and 46% longer travel in downstream locomotion policies.
Natural humanoid robot locomotion with generative motion prior
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Reinforcement learning with a constellation-based reward enables direct, efficient humanoid locomotion to short-range SE(2) targets, outperforming velocity-tracking baselines in simulation and transferring to hardware.
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Trajectory-based actuator identification via differentiable simulation
Differentiable simulation enables torque-sensor-free actuator model identification from trajectory data, achieving 1.88x better position tracking than a stand-trained baseline and 46% longer travel in downstream locomotion policies.
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No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets
Reinforcement learning with a constellation-based reward enables direct, efficient humanoid locomotion to short-range SE(2) targets, outperforming velocity-tracking baselines in simulation and transferring to hardware.