Sim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation Injection
This paper proposes a novel alternative to existing sim-to-real methods for training control policies with simulated experiences. Unlike prior methods that typically rely on domain randomization over a fixed finite set of parameters, the proposed approach injects state-dependent perturbations into the input joint torque during forward simulation. These perturbations are designed to simulate a broader spectrum of reality gaps than standard parameter randomization without requiring additional training. By using neural networks as flexible perturbation generators, the proposed method can represent complex, state-dependent uncertainties, such as nonlinear actuator dynamics and contact complia...
| Authors | Junhyeok Rui Cha, Woohyun Cha, Jaeyong Shin et al. |
|---|---|
| Primary category | cs.RO |
| Published | 23 March 2026 |
| Last updated at source | 25 March 2026 |
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