Legged locomotion is arguably the most suited and versatile mode to deal with natural or unstructured terrains. Intensive research into dynamic walking and running controllers has recently yielded great advances, both in the optimal control and reinforcement learning (RL) literature. Hopping is a challenging dynamic task involving a flight phase and has the potential to increase the traversability of legged robots. Model based control for hopping typically relies on accurate detection of different jump phases, such as lift-off or touch down, and using different controllers for each phase. In this paper, we present a end-to-end RL based torque controller that learns to implicitly detect the relevant jump phases, removing the need to provide manual heuristics for state detection. We also extend a method for simulation to reality transfer of the learned controller to contact rich dynamic tasks, resulting in successful deployment on the robot after training without parameter tuning.

翻译：腿部运动可以说是处理自然或非结构化地形最为适配且多能的运动方式。近年来，对动态行走与跑步控制器的深入研究在最优控制与强化学习领域均取得了重大进展。跳跃是一项涉及飞行阶段的动态挑战性任务，有望提升腿式机器人的越障能力。基于模型的控制方法通常依赖于对不同跳跃阶段（如离地或触地）的精确检测，并为每个阶段使用不同的控制器。本文提出了一种基于强化学习的端到端力矩控制器，能够隐式学习检测相关跳跃阶段，从而无需提供人工设定的状态检测启发式规则。我们还扩展了一种将所学控制器从仿真迁移至现实的方法，使其适用于接触丰富的动态任务，最终在无需参数调优的情况下，成功将训练后的控制器部署于机器人上。