障害物回避のためのバリア証明付き多項式力学系の学習
Learning Barrier-Certified Polynomial Dynamical Systems for Obstacle Avoidance with Robots
ロボットの障害物回避のため、バリア証明を組み込んだ最適化で安定な力学系を学習する手法を提案。多項式表現とSOS最適化により、従来より複雑な障害物形状に対応可能。
著者: Martin Schonger, Hugo T. M. Kussaba, Lingyun Chen, Luis Figueredo, Abdalla Swikir, Aude Billard, Sami Haddadin
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Established techniques that enable robots to learn from demonstrations are based on learning a stable dynamical system (DS). To increase the robots' resilience to perturbations during tasks that involve static obstacle avoidance, we propose incorporating barrier certificates into an optimization problem to learn a stable and barrier-certified DS. Such optimization problem can be very complex or extremely conservative when the traditional linear parameter-varying formulation is used. Thus, different from previous approaches in the literature, we propose to use polynomial representations for DSs, which yields an optimization problem that can be tackled by sum-of-squares techniques. Finally, our approach can handle obstacle shapes that fall outside the scope of assumptions typically found in the literature concerning obstacle avoidance within the DS learning framework. Supplementary material can be found at the project webpage: https://martinschonger.github.io/abc-ds
