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arXiv · 2504.06585

Sim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation Injection

Junhyeok Rui Cha·Woohyun Cha·Jaeyong Shin·Donghyeon Kim·Jaeheung Park
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Abstract

This paper proposes a novel alternative to existing sim-to-real methods for training control policies with simulated experiences. Prior sim-to-real methods for legged robots mostly rely on the domain randomization approach, where a fixed finite set of simulation parameters is randomized during training. Instead, our method adds state-dependent perturbations to the input joint torque used for forward simulation during the training phase. These state-dependent perturbations are designed to simulate a broader range of reality gaps than those captured by randomizing a fixed set of simulation parameters. Experimental results show that our method enables humanoid locomotion policies that achieve greater robustness against complex reality gaps unseen in the training domain.

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Junhyeok Rui Cha, Woohyun Cha, Jaeyong Shin, Donghyeon Kim, Jaeheung Park. 2025-04-09. Sim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation Injection. https://arxiv.org/abs/2504.06585

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