微分動的計画法によるスパース性誘導最適制御
Sparsity-Inducing Optimal Control via Differential Dynamic Programming
DDPベースの最適制御にL1・Huber正則化を導入し、衛星やヒューマノイドの制御でスパースな制御入力を実現する手法を提案・検証した。
著者: Traiko Dinev, Wolfgang Merkt, Vladimir Ivan, Ioannis Havoutis, Sethu Vijayakumar
分類: cs.RO
原文アブストラクト
Optimal control is a popular approach to synthesize highly dynamic motion. Commonly, $L_2$ regularization is used on the control inputs in order to minimize energy used and to ensure smoothness of the control inputs. However, for some systems, such as satellites, the control needs to be applied in sparse bursts due to how the propulsion system operates. In this paper, we study approaches to induce sparsity in optimal control solutions -- namely via smooth $L_1$ and Huber regularization penalties. We apply these loss terms to state-of-the-art DDP-based solvers to create a family of sparsity-inducing optimal control methods. We analyze and compare the effect of the different losses on inducing sparsity, their numerical conditioning, their impact on convergence, and discuss hyperparameter settings. We demonstrate our method in simulation and hardware experiments on canonical dynamics systems, control of satellites, and the NASA Valkyrie humanoid robot. We provide an implementation of our method and all examples for reproducibility on GitHub.
