Reinforcement learning (RL) provides much potential for locomotion of legged robot. Due to the gap between simulation and the real world, achieving sim-to-real for legged robots is challenging. However, the support polygon of legged robots can help to overcome some of these challenges. Quadruped robot has a considerable support polygon, followed by bipedal robot with actuated feet, and point-footed bipedal robot has the smallest support polygon. Therefore, despite the existing sim-to-real gap, most of the recent RL approaches are deployed to the real quadruped robots that are inherently more stable, while the RL-based locomotion of bipedal robot is challenged by zero-shot sim-to-real task. Especially for the point-footed one that gets better dynamic performance, the inevitable tumble brings extra barriers to sim-to-real task. Actually, the crux of this type of problem is the difference of mechanics properties between the physical robot and the simulated one, making it difficult to play the learned skills well on the physical bipedal robot. In this paper, we introduce the embedded mechanics properties (EMP) based on the optimization with Gaussian processes to RL training, making it possible to perform sim-to-real transfer on the BRS1-P robot used in this work, hence the trained policy can be deployed on the BRS1-P without any struggle. We validate the performance of the learning-based BRS1-P on the condition of disturbances and terrains not ever learned, demonstrating the bipedal locomotion and resistant performance.

强化学习（RL）为腿式机器人的运动提供了很大的潜力。由于模拟与现实世界之间的差距，实现腿式机器人的拟真具有挑战性。然而，腿式机器人的支撑多边形可以帮助克服其中一些挑战。四足机器人具有相当大的支撑多边形，其次是具有驱动脚的双足机器人，而点足双足机器人具有最小的支撑多边形。因此，尽管存在模拟与真实的差距，但最近大多数强化学习方法都部署到本质上更稳定的真实四足机器人上，而基于强化学习的双足机器人运动受到零样本模拟与真实的挑战。真正的任务。特别是对于那些获得更好动态性能的尖足动物来说，不可避免的翻滚给模拟任务到真实任务带来了额外的障碍。其实，这类问题的症结在于实体机器人与模拟机器人的力学特性存在差异，导致所学的技能很难在实体双足机器人上得到很好的发挥。在本文中，我们将基于高斯过程优化的嵌入式力学特性（EMP）引入到强化学习训练中，使得在本工作中使用的 BRS1-P 机器人上执行模拟到真实的迁移成为可能，从而得到训练策略可以毫无困难地部署在 BRS1-P 上。我们在从未学习过的干扰和地形条件下验证了基于学习的 BRS1-P 的性能，展示了双足运动和抵抗性能。