異種マルチロボット系による3D不確実環境でのメタ強化学習に基づくセンサスキャン
Meta Reinforcement Learning Based Sensor Scanning in 3D Uncertain Environments for Heterogeneous Multi-Robot Systems
異種マルチロボットシステムで3D不確実環境のセンサスキャンを行うため、メタ学習で探索と適応を高速化する手法を提案し、成功率と適応速度で既存手法を上回った。
著者: Junfeng Chen, Yuan Gao, Junjie Hu, Fuqin Deng, Tin Lun Lam
分類: cs.RO
原文アブストラクト
We study a novel problem that tackles learning based sensor scanning in 3D and uncertain environments with heterogeneous multi-robot systems. Our motivation is two-fold: first, 3D environments are complex, the use of heterogeneous multi-robot systems intuitively can facilitate sensor scanning by fully taking advantage of sensors with different capabilities. Second, in uncertain environments (e.g. rescue), time is of great significance. Since the learning process normally takes time to train and adapt to a new environment, we need to find an effective way to explore and adapt quickly. To this end, in this paper, we present a meta-learning approach to improve the exploration and adaptation capabilities. The experimental results demonstrate our method can outperform other methods by approximately 15%-27% on success rate and 70%-75% on adaptation speed.
