火星跳跃机器人Olympus用强化学习实现空中姿态控制，提升探索效率。
Olympus: A Jumping Quadruped for Planetary Exploration Utilizing Reinforcement Learning for In-Flight Attitude Control
- 基于强化学习实现跳跃中的空中姿态调整
- 在模拟中优化设计，实现高跳高远的运动性能
- 成功跨越仿真到现实的差距，实测验证姿态控制效果
在低重力行星如月球和火星上，腿式机器人可利用跳跃高效移动，相较传统巡视器更具优势。本文提出专为火星重力环境设计的跳跃型四足机器人Olympus，通过仿真优化其腿部结构与整体构型，以实现垂直跳跃高度、前向跳跃距离及空中姿态重定向的最大化。随后，采用强化学习策略实现精确的“飞行中”姿态控制。通过大量实验验证，成功跨越仿真到现实的鸿沟，完成了姿态重定向的实地测试。
原文摘要 · Abstract (English)
Exploring planetary bodies with lower gravity, such as the moon and Mars, allows legged robots to utilize jumping as an efficient form of locomotion thus giving them a valuable advantage over traditional rovers for exploration. Motivated by this fact, this paper presents the design, simulation, and learning-based "in-flight" attitude control of Olympus, a jumping legged robot tailored to the gravity of Mars. First, the design requirements are outlined followed by detailing how simulation enabled optimizing the robot's design - from its legs to the overall configuration - towards high vertical jumping, forward jumping distance, and in-flight attitude reorientation. Subsequently, the reinforcement learning policy used to track desired in-flight attitude maneuvers is presented. Successfully crossing the sim2real gap, extensive experimental studies of attitude reorientation tests are demonstrated.
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