通过注入关节力矩扰动，提升人形机器人仿真到现实的迁移能力。
Sim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation Injection
- 在仿真中动态注入与状态相关的力矩扰动，模拟真实世界复杂不确定性。
- 无需额外训练即可在真实环境中实现更优的行走鲁棒性。
- 适合需要高可靠性仿真实现的机器人控制研究者。
本文提出一种新颖的仿真到现实迁移方法，用于训练基于仿真经验的控制策略。与以往依赖固定参数集进行领域随机化的方法不同，该方法在前向仿真过程中向输入关节力矩注入状态相关扰动。这些扰动旨在模拟比传统参数随机化更广泛的现实差距，且无需额外训练。利用神经网络作为灵活的扰动生成器，该方法可表征非线性执行器动力学、接触柔顺性等复杂状态依赖不确定性，而这是参数随机化无法捕捉的。实验结果表明，所提方法使人体形态机器人行走策略在仿真和真实部署中均对复杂未见现实差距展现出更优的鲁棒性。
原文摘要 · Abstract (English)
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 compliance, that parametric randomization cannot capture. Experimental results demonstrate that the proposed approach enables humanoid locomotion policies to achieve superior robustness against complex, unseen reality gaps in both simulation and real-world deployment.
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