計算コストの高い信念遷移を持つPOMDPに対する遅延ヒューリスティック探索
Lazy Heuristic Search for Solving POMDPs with Expensive-to-Compute Belief Transitions
信念遷移の計算が高コストなPOMDPに対し、Q値推定を用いて遷移計算を遅延させるLazy RTDP-BelとLazy LAO*を提案し、接触を伴うマニピュレーションや屋外・屋内ナビゲーションで計画時間を大幅に短縮した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Muhammad Suhail Saleem, Rishi Veerapaneni, Maxim Likhachev
分類: cs.RO
原文アブストラクト
Heuristic search solvers like RTDP-Bel and LAO* have proven effective for computing optimal and bounded sub-optimal solutions for Partially Observable Markov Decision Processes (POMDPs), which are typically formulated as belief MDPs. A belief represents a probability distribution over possible system states. Given a parent belief and an action, computing belief state transitions involves Bayesian updates that combine the transition and observation models of the POMDP to determine successor beliefs and their transition probabilities. However, there is a class of problems, specifically in robotics, where computing these transitions can be prohibitively expensive due to costly physics simulations, raycasting, or expensive collision checks required by the underlying transition and observation models, leading to long planning times. To address this challenge, we propose Lazy RTDP-Bel and Lazy LAO*, which defer computing expensive belief state transitions by leveraging Q-value estimation, significantly reducing planning time. We demonstrate the superior performance of the proposed lazy planners in domains such as contact-rich manipulation for pose estimation, outdoor navigation in rough terrain, and indoor navigation with a 1-D LiDAR sensor. Additionally, we discuss practical Q-value estimation techniques for commonly encountered problem classes that our lazy planners can leverage. Our results show that lazy heuristic search methods dramatically improve planning speed by postponing expensive belief transition evaluations while maintaining solution quality.
関連論文
- 性能保証付きρ-POMDPにおける観測空間分割による意思決定の効率化POMDP/計画
- POMDPの粒子信念近似における最適性保証POMDP/計画
- POMDPにおけるほぼ確実な到達可能性の強制POMDP/計画
- pomdp_py：POMDP問題を構築・求解するフレームワークPOMDP/計画
- DualSMC: 連続POMDPにおける微分可能なフィルタリングと計画のトンネリングPOMDP/計画
- マルチレベルモンテカルロによるPOMDPのオンライン解法POMDP/計画
