Recent advancements in legged locomotion research have made legged robots a preferred choice for navigating challenging terrains when compared to their wheeled counterparts. This paper presents a novel locomotion policy, trained using Deep Reinforcement Learning, for a quadrupedal robot equipped with an additional prismatic joint between the knee and foot of each leg. The training is performed in NVIDIA Isaac Gym simulation environment. Our study investigates the impact of these joints on maintaining the quadruped's desired height and following commanded velocities while traversing challenging terrains. We provide comparison results, based on a Cost of Transport (CoT) metric, between quadrupeds with and without prismatic joints. The learned policy is evaluated on a set of challenging terrains using the CoT metric in simulation. Our results demonstrate that the added degrees of actuation offer the locomotion policy more flexibility to use the extra joints to traverse terrains that would be deemed infeasible or prohibitively expensive for the conventional quadrupedal design, resulting in significantly improved efficiency.

翻译：腿式运动研究的最新进展使得腿式机器人在穿越挑战性地形时相较于轮式机器人成为更优选择。本文提出了一种基于深度强化学习训练的新型运动策略，适用于每条腿在膝关节与足部之间额外配备一个棱柱关节的四足机器人。训练过程在NVIDIA Isaac Gym仿真环境中完成。本研究探究了这些关节在维持四足机器人期望高度并跟随指令速度穿越挑战性地形时的影响。我们基于运输成本（CoT）指标，提供了配备与未配备棱柱关节的四足机器人之间的对比结果。该学习策略在仿真环境中使用CoT指标对一组挑战性地形进行评价。结果表明，额外的驱动自由度使运动策略能够更灵活地利用该关节穿越传统四足设计难以实现或代价高昂的地形，从而显著提升运动效率。