In this paper, we consider a general task of jumping varying distances and heights for a quadrupedal robot in noisy environments, such as off of uneven terrain and with variable robot dynamics parameters. To accurately jump in such conditions, we propose a framework using deep reinforcement learning that leverages and augments the complex solution of nonlinear trajectory optimization for quadrupedal jumping. While the standalone optimization limits jumping to take-off from flat ground and requires accurate assumptions of robot dynamics, our proposed approach improves the robustness to allow jumping off of significantly uneven terrain with variable robot dynamical parameters and environmental conditions. Compared with walking and running, the realization of aggressive jumping on hardware necessitates accounting for the motors' torque-speed relationship as well as the robot's total power limits. By incorporating these constraints into our learning framework, we successfully deploy our policy sim-to-real without further tuning, fully exploiting the available onboard power supply and motors. We demonstrate robustness to environment noise of foot disturbances of up to 6 cm in height, or 33% of the robot's nominal standing height, while jumping 2x the body length in distance.

翻译：本文研究了四足机器人在噪声环境（如不平整地形及动态参数变化）中完成不同距离和高度跳跃的通用任务。为实现精确跳跃，我们提出了一种基于深度强化学习的框架，该框架利用并增强了非线性轨迹优化针对四足跳跃的复杂解。尽管纯优化方法仅允许从平坦地面起跳，且需精确假设机器人动力学参数，但本文方法提升了鲁棒性，使机器人能够在显著不平整地形上跳跃，并适应可变的动力学参数与环境条件。与行走和奔跑相比，实现硬件上的激进跳跃需考虑电机扭矩-速度关系及机器人总功率限制。通过将上述约束融入学习框架，我们成功将策略从仿真迁移至真实机器人而无需额外调整，充分开发了机载电源与电机的潜力。实验表明：在跳跃距离达机身长度2倍时，机器人对高达6厘米（占机器人标称站立高度33%）的足端高度噪声扰动仍保持鲁棒。