e-Article
Learning and Planning for Temporally Extended Tasks in Unknown Environments
Document Type
Conference
Author
Source
2021 IEEE International Conference on Robotics and Automation (ICRA) Robotics and Automation (ICRA), 2021 IEEE International Conference on. :4830-4836 May, 2021
Subject
Language
ISSN
2577-087X
Abstract
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.