深層強化学習と教師あり学習を統合した屋内マッピングの高速化
Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping
深層強化学習による移動計画と生成モデルによる地図予測を組み合わせ、屋内環境のマッピング時間を最大4倍短縮した研究。
著者: Elchanan Zwecher, Eran Iceland, Sean R. Levy, Shmuel Y. Hayoun, Oren Gal, Ariel Barel
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
The challenge of mapping indoor environments is addressed. Typical heuristic algorithms for solving the motion planning problem are frontier-based methods, that are especially effective when the environment is completely unknown. However, in cases where prior statistical data on the environment's architectonic features is available, such algorithms can be far from optimal. Furthermore, their calculation time may increase substantially as more areas are exposed. In this paper we propose two means by which to overcome these shortcomings. One is the use of deep reinforcement learning to train the motion planner. The second is the inclusion of a pre-trained generative deep neural network, acting as a map predictor. Each one helps to improve the decision making through use of the learned structural statistics of the environment, and both, being realized as neural networks, ensure a constant calculation time. We show that combining the two methods can shorten the duration of the mapping process by up to 4 times, compared to frontier-based motion planning.
