モデル予測木探索：再利用可能な木探索によるサンプル効率の良い後退ホライズン計画
Model Predictive Trees: Sample-Efficient Receding Horizon Planning with Reusable Tree Search
前回の最適部分木全体を再利用することで探索を効率化する後退ホライズン木探索アルゴリズムを提案し、自動運転車による物体押し操作タスクで有効性を示した。
著者: John Lathrop, Benjamin Rivi`ere, Jedidiah Alindogan, Soon-Jo Chung
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
We present Model Predictive Trees (MPT), a receding horizon tree search algorithm that improves its performance by reusing information efficiently. Whereas existing solvers reuse only the highest-quality trajectory from the previous iteration as a "hotstart", our method reuses the entire optimal subtree, enabling the search to be simultaneously guided away from the low-quality areas and towards the high-quality areas. We characterize the restrictions on tree reuse by analyzing the induced tracking error under time-varying dynamics, revealing a tradeoff between the search depth and the timescale of the changing dynamics. In numerical studies, our algorithm outperforms state-of-the-art sampling-based cross-entropy methods with hotstarting. We demonstrate our planner on an autonomous vehicle testbed performing a nonprehensile manipulation task: pushing a target object through an obstacle field. Code associated with this work will be made available at https://github.com/jplathrop/mpt.
