高速状態空間サンプリングによる長期的オンラインPOMDP計画のスケーリング
Scaling Long-Horizon Online POMDP Planning via Rapid State Space Sampling
ロボットの不確実性下の計画で使われるPOMDPについて、状態空間を高速サンプリングしてマクロ行動を生成し、長期的な計画を効率的に解くオンラインソルバROP-RaS3を提案した。100ステップ超の計画や15次元状態空間の問題で既存手法を大幅に上回る。
著者: Yuanchu Liang, Edward Kim, Wil Thomason, Zachary Kingston, Hanna Kurniawati, Lydia E. Kavraki
分類: cs.RO
原文アブストラクト
Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalability of POMDP solvers, long-horizon POMDPs (e.g., $\geq15$ steps) remain difficult to solve. This paper proposes a new approximate online POMDP solver, called Reference-Based Online POMDP Planning via Rapid State Space Sampling (ROP-RaS3). ROP-RaS3 uses novel extremely fast sampling-based motion planning techniques to sample the state space and generate a diverse set of macro actions online which are then used to bias belief-space sampling and infer high-quality policies without requiring exhaustive enumeration of the action space -- a fundamental constraint for modern online POMDP solvers. ROP-RaS3 is evaluated on various long-horizon POMDPs, including on a problem with a planning horizon of more than 100 steps and a problem with a 15-dimensional state space that requires more than 20 look ahead steps. In all of these problems, ROP-RaS3 substantially outperforms other state-of-the-art methods by up to multiple folds.
