用强化学习让四足机器人在火星低重力下跳着走，还能空中调姿安全落地。
Towards Low-Gravity Planetary Exploration using Reinforcement Learning for Walking, Jumping, and In-flight Attitude Control
- 基于五杆腿设计，用强化学习实现行走、跳跃和空中姿态控制
- 模拟显示可完成3.1米垂直跳和3.9米前跳，空中调姿2.6秒转90°
- 适合火星探测任务中的复杂地形自主移动，具备真实机器人验证能力
本文提出适用于行星探测场景的强化学习（RL）策略，用于动态四足机器人的运动。基于优化设计的五杆腿四足机器人，开发了行走、垂直跳跃、前向跳跃及飞行中姿态控制的RL策略，专为火星低重力环境定制。这些策略协同使机器人能够跨越超过自身高度的障碍，并通过精确的空中姿态调整实现安全着陆。我们通过单轴姿态调整实验实现了从仿真到现实（Sim2Real）的转移验证，态度控制策略在Olympus四足机器人上成功应用；其余运动策略均在仿真中验证。完整火星探测任务场景展示了多策略协同部署能力。实验结果表明，可在2.6秒内完成90°姿态调整；仿真结果显示，在火星重力条件下可实现3.1米垂直跳跃和3.9米前向跳跃。
原文摘要 · Abstract (English)
This paper presents reinforcement learning (RL) policies for dynamic quadrupedal locomotion in planetary exploration scenarios. Building on a taskoptimized quadruped with a 5-bar leg design, we develop RL policies for walking, vertical jumping, forward jumping, and in-flight attitude control, explicitly tailored to the reduced gravity on Mars. These policies jointly enable such robots to overcome obstacles larger than themselves through coordinated jumping and precise in-flight reorientation for safe landings. We demonstrate Sim2Real transfer of the attitude control policy on the Olympus quadruped through single-axis reorientation tests, while all locomotion policies are validated in simulation. A complete Mars exploration mission scenario demonstrates coordinated policy deployment across challenging terrain. Experimental results show 90° attitude reorientation in 2.6 seconds, with simulations demonstrating 3.1 meter vertical jumps and 3.9 meter forward jumps under Martian gravity conditions. - Supplementary video: https://www.youtube.com/watch?v=qlSJ3P87A4A
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