Sim-to-Real Learning of Robust Compliant Bipedal Locomotion on Torque Sensor-Less Gear-Driven Humanoid

Masuda, Shimpei; Takahashi, Kuniyuki

doi:10.48550/arxiv.2204.03897

Cited by 1 publication

(1 citation statement)

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“…Recent works have produced behaviors including walking and running (24,25), stair climbing (26), and jumping (27). Most recent works have focused on high-quality, full-sized bipeds and humanoids, with a much smaller number (48,(52)(53)(54) targeting more basic platforms whose simpler and less precise actuators and sensors pose additional challenges in terms of sim-to-real transfer. In addition, there is a growing interest in whole-body control, that is, tasks in which the whole body is used in flexible ways to interact with the environment.…”

Section: Discussionmentioning

confidence: 99%

Learning agile soccer skills for a bipedal robot with deep reinforcement learning

Haarnoja,

Moran,

Lever

et al. 2024

Sci. Robot.

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We investigated whether deep reinforcement learning (deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies. We used deep RL to train a humanoid robot to play a simplified one-versus-one soccer game. The resulting agent exhibits robust and dynamic movement skills, such as rapid fall recovery, walking, turning, and kicking, and it transitions between them in a smooth and efficient manner. It also learned to anticipate ball movements and block opponent shots. The agent’s tactical behavior adapts to specific game contexts in a way that would be impractical to manually design. Our agent was trained in simulation and transferred to real robots zero-shot. A combination of sufficiently high-frequency control, targeted dynamics randomization, and perturbations during training enabled good-quality transfer. In experiments, the agent walked 181% faster, turned 302% faster, took 63% less time to get up, and kicked a ball 34% faster than a scripted baseline.

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