制御収縮計量による安定化可能な力学系の学習
Learning Stabilizable Dynamical Systems via Control Contraction Metrics
制御収縮理論に基づく正則化を導入し、学習した力学系が常に安定化可能な制御器と組み合わせられることを保証する半教師あり学習手法を提案。少数のデモでも軌道生成・追従性能が向上することを平面クアッドロータで示した。
著者: Sumeet Singh, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone
分類: eess.SY, cs.LG, cs.RO, cs.SY, math.OC
原文アブストラクト
We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics. The key idea is to develop a new control-theoretic regularizer for dynamics fitting rooted in the notion of stabilizability, which guarantees that the learned system can be accompanied by a robust controller capable of stabilizing any open-loop trajectory that the system may generate. By leveraging tools from contraction theory, statistical learning, and convex optimization, we provide a general and tractable semi-supervised algorithm to learn stabilizable dynamics, which can be applied to complex underactuated systems. We validated the proposed algorithm on a simulated planar quadrotor system and observed notably improved trajectory generation and tracking performance with the control-theoretic regularized model over models learned using traditional regression techniques, especially when using a small number of demonstration examples. The results presented illustrate the need to infuse standard model-based reinforcement learning algorithms with concepts drawn from nonlinear control theory for improved reliability.
