未知環境における時間的に拡張されたタスクの学習と計画
Learning and Planning for Temporally Extended Tasks in Unknown Environments
部分的にしか見えていない環境で時相論理タスクを達成するため、高レベル行動の成功確率とコストをニューラルネットで推定し、最小期待コスト計画を探索する手法を提案した。
著者: Christopher Bradley, Adam Pacheck, Gregory J. Stein, Sebastian Castro, Hadas Kress-Gazit, Nicholas Roy
分類: cs.RO
原文アブストラクト
We propose a novel planning technique for satisfying tasks specified in temporal logic in partially revealed environments. We define high-level actions derived from the environment and the given task itself, and estimate how each action contributes to progress towards completing the task. As the map is revealed, we estimate the cost and probability of success of each action from images and an encoding of that action using a trained neural network. These estimates guide search for the minimum-expected-cost plan within our model. Our learned model is structured to generalize across environments and task specifications without requiring retraining. We demonstrate an improvement in total cost in both simulated and real-world experiments compared to a heuristic-driven baseline.
