幾何学的幻覚から学習する動作計画器
Extended Abstract: Motion Planners Learned from Geometric Hallucination
障害物のない空間での計画が最適となる障害物配置を逆算的に生成(幻覚)し、それを用いて実環境の混雑空間で動くエンドツーエンド動作計画器を学習・実機検証した論文。
著者: Xuesu Xiao, Bo Liu, Peter Stone
分類: cs.RO
原文アブストラクト
Learning motion planners to move robot from one point to another within an obstacle-occupied space in a collision-free manner requires either an extensive amount of data or high-quality demonstrations. This requirement is caused by the fact that among the variety of maneuvers the robot can perform, it is difficult to find the single optimal plan without many trial-and-error or an expert who is already capable of doing so. However, given a plan performed in obstacle-free space, it is relatively easy to find an obstacle geometry, where this plan is optimal. We consider this "dual" problem of classical motion planning and name this process of finding appropriate obstacle geometry as hallucination. In this work, we present two different approaches to hallucinate (1) the most constrained and (2) a minimal obstacle space where a given plan executed during an exploration phase in a completely safe obstacle-free environment remains optimal. We then train an end-to-end motion planner that can produce motions to move through realistic obstacles during deployment. Both methods are tested on a physical mobile robot in real-world cluttered environments.
