Olympus：強化学習による飛行中姿勢制御を用いた惑星探査用跳躍四脚ロボット
Olympus: A Jumping Quadruped for Planetary Exploration Utilizing Reinforcement Learning for In-Flight Attitude Control
火星の低重力環境での跳躍移動を目指し、シミュレーションで設計最適化した四脚ロボットOlympusを開発し、強化学習による飛行中の姿勢制御を実機で検証した。
著者: Jørgen Anker Olsen, Grzegorz Malczyk, Kostas Alexis
分類: cs.RO, cs.LG
原文アブストラクト
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.
