Recently, reinforcement learning has become a promising and polular solution for robot legged locomotion. Compared to model-based control, reinforcement learning based controllers can achieve better robustness against uncertainties of environments through sim-to-real learning. However, the corresponding learned gaits are in general overly conservative and unatural. In this paper, we propose a new framework for learning robust, agile and natural legged locomotion skills over challenging terrain. We incorporate an adversarial training branch based on real animal locomotion data upon a teacher-student training pipeline for robust sim-to-real transfer. Empirical results on both simulation and real world of a quadruped robot demonstrate that our proposed algorithm enables robustly traversing challenging terrains such as stairs, rocky ground and slippery floor with only proprioceptive perception. Meanwhile, the gaits are more agile, natural, and energy efficient compared to the baselines. Both qualitative and quantitative results are presented in this paper.

翻译：最近，强化学习已成为机器人腿部运动领域一种有前景且流行的解决方案。与基于模型的控制相比，基于强化学习的控制器通过仿真到现实的学习，能够更好地应对环境不确定性，实现更强的鲁棒性。然而，相应学习到的步态通常过于保守且不自然。在本文中，我们提出了一种新框架，用于在复杂地形上学习鲁棒、敏捷且自然的腿部运动技能。我们在教师-学生训练流程的基础上，融入基于真实动物运动数据的对抗训练分支，以实现鲁棒的仿真到现实迁移。在四足机器人的仿真和现实世界中进行的实验结果表明，我们提出的算法仅凭本体感觉感知，就能鲁棒地穿越楼梯、岩石地面和湿滑地板等复杂地形。同时，与基线方法相比，步态更加敏捷、自然且能效更高。本文展示了定性及定量两种结果。