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.

翻译：本文提出了一种新颖的替代方案，用于通过仿真经验训练控制策略的仿真到现实迁移方法。现有针对腿式机器人的仿真到现实迁移方法主要依赖于领域随机化方法，即在训练过程中随机化一组固定的有限仿真参数。相反，我们的方法在训练阶段，向用于前向仿真输入的关节扭矩添加状态依赖的扰动。这些状态依赖扰动旨在模拟比随机化固定仿真参数集所覆盖的更广泛现实差距。实验结果表明，我们的方法使得人形机器人运动策略能够对训练领域未见过的复杂现实差距实现更强的鲁棒性。