教師あり学習による液体移送のフィードバック動作計画
Feedback Motion Planning for Liquid Transfer using Supervised Learning
ニューラルネットで液体の動特性を学習し、再cedingホライズン最適化で液体を容器間でこぼさず移す動作計画手法を提案した論文。
著者: Zherong Pan, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present a novel motion planning algorithm for transferring a liquid body from a source to a target container. Our approach uses a receding-horizon optimization strategy that takes into account fluid constraints and avoids collisions. In order to efficiently handle the high-dimensional configuration space of a liquid body, we use system identification to learn its dynamics characteristics using a neural network. We generate the training dataset using stochastic optimization in a transfer-problem-specific search space. The runtime feedback motion planner is used for real-time planning and we observe high success rate in our simulated 2D and 3D fluid transfer benchmarks.
