不確実性下におけるハイブリッド制御の軌道最適化
Hybrid control trajectory optimization under uncertainty
離散と連続が混在するハイブリッド制御の軌道最適化をDDPに離散行動を組み込んで解き、不確実性下のPOMDP軌道計画にも拡張した研究。
著者: Joni Pajarinen, Ville Kyrki, Michael Koval, Siddhartha Srinivasa, Jan Peters, Gerhard Neumann
分類: cs.RO
原文アブストラクト
Trajectory optimization is a fundamental problem in robotics. While optimization of continuous control trajectories is well developed, many applications require both discrete and continuous, i.e., hybrid, controls. Finding an optimal sequence of hybrid controls is challenging due to the exponential explosion of discrete control combinations. Our method, based on Differential Dynamic Programming (DDP), circumvents this problem by incorporating discrete actions inside DDP: we first optimize continuous mixtures of discrete actions, and, subsequently force the mixtures into fully discrete actions. Moreover, we show how our approach can be extended to partially observable Markov decision processes (POMDPs) for trajectory planning under uncertainty. We validate the approach in a car driving problem where the robot has to switch discrete gears and in a box pushing application where the robot can switch the side of the box to push. The pose and the friction parameters of the pushed box are initially unknown and only indirectly observable.
