STRIDE: Learning Humanoid Locomotion in Minutes with Acceleration-Based Rewards
Overview
This paper introduces STRIDE, a method for learning humanoid locomotion directly on physical robots by using acceleration-based rewards instead of base velocity tracking. By combining off-policy reinforcement learning (XQC), high-gain joint controllers, and symmetry augmentation, STRIDE learns to walk on Unitree G1 and Booster T1 humanoids in simulation within 14 minutes of samples. The approach reduces training falls by 81% compared to methods without these components, which typically require over an hour to learn.
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Environment: simulation
“otion from minutes of samples with a reward that only requires the onboard Inertial Measurement Unit (IMU). Instead of tracking the base velocity, STRIDE rewards the robot for following a commanded acceleration profile. To learn efficiently from this reward, we use the off-policy RL algorithm XQC and combine a high gain setting for the joint position controllers with left-right symmetry augmentation. In simulation, S”
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Reported quantity · Reported duration: 14 other · Basis not reported
“TRIDE learns to walk on the Unitree G1 and Booster T1 humanoids within 14 minutes of samples. Compared to learning without these two components, it reduces the falls during training by 81%, while without them the robot learns only in over an hour.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSTRIDE: Learning Humanoid Locomotion in Minutes with Acceleration-Based Rewards
This paper introduces STRIDE, a method for learning humanoid locomotion directly on physical robots by using acceleration-based rewards instead of base velocity tracking. By combining off-policy reinforcement learning (XQC), high-gain joint controllers, and symmetry augmentation, STRIDE learns to walk on Unitree G1 and Booster T1 humanoids in simulation within 14 minutes of samples. The approach reduces training falls by 81% compared to methods without these components, which typically require over an hour to learn.
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