四足ロボットの全方向3Dジャンプのための誘導強化学習
Guided Reinforcement Learning for Omnidirectional 3D Jumping in Quadruped Robots
ベジェ曲線と等加速度直線運動モデルを組み合わせて物理的直感を活用し、四足ロボットの全方向3Dジャンプを効率的かつ説明可能に学習する誘導強化学習手法を提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Riccardo Bussola, Michele Focchi, Giulio Turrisi, Claudio Semini, Luigi Palopoli
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Jumping poses a significant challenge for quadruped robots, despite being crucial for many operational scenarios. While optimisation methods exist for controlling such motions, they are often time-consuming and demand extensive knowledge of robot and terrain parameters, making them less robust in real-world scenarios. Reinforcement learning (RL) is emerging as a viable alternative, yet conventional end-to-end approaches lack efficiency in terms of sample complexity, requiring extensive training in simulations, and predictability of the final motion, which makes it difficult to certify the safety of the final motion. To overcome these limitations, this paper introduces a novel guided reinforcement learning approach that leverages physical intuition for efficient and explainable jumping, by combining B\'ezier curves with a Uniformly Accelerated Rectilinear Motion (UARM) model. Extensive simulation and experimental results clearly demonstrate the advantages of our approach over existing alternatives.
