狭い空間を跳び越える四足ロボットの高ダイナミックスキル遷移学習
Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space
四足ロボットが狭いゲートを動的に通過するための階層型強化学習パイプラインを提案。低レベル方策は模倣学習で動物の多様なスキルを獲得し、高レベル制御が視覚情報とスキル能力を考慮して衝突回避軌道を選択する。
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著者: Zeren Luo, Jiahui Zhang, Yimin Han, Ji Ma, Minghao Lu, Ioannis Havoutis, Peng Lu
分類: cs.RO
原文アブストラクト
Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating this behavior in quadrupedal robots has been a longstanding challenge. Here, we propose a hierarchical reinforcement learning pipeline that empowers the robots to perform aggressive locomotion through constrained obstacles--a narrow gate. The imitation learning technique is used to train the low-level policy, which mimics the behaviors of real animals and forms a set of diverse skills. The high-level controller, having an awareness of the capability of low-level skills and acquiring the gate information via vision-based detection, determines the suitable maneuvers with collision-free trajectories to traverse it dynamically. Notably, we also verify that this framework can be extended to other highly dynamic tasks. This is one of the first works that perform autonomous and agile aerial gate traversal tasks on ground-walking robots, extending the lifelike agility of legged robots to match that of their biological counterparts.