Generalizable Imitation Learning from Observation via Inferring Goal Proximity
Journal
Advances in Neural Information Processing Systems
Journal Volume
20
Pages
16118-16130
Date Issued
2021
Author(s)
Abstract
Task progress is intuitive and readily available task information that can guide an agent closer to the desired goal. Furthermore, a task progress estimator can generalize to new situations. From this intuition, we propose a simple yet effective imitation learning from observation method for a goal-directed task using a learned goal proximity function as a task progress estimator for better generalization to unseen states and goals. We obtain this goal proximity function from expert demonstrations and online agent experience, and then use the learned goal proximity as a dense reward for policy training. We demonstrate that our proposed method can robustly generalize compared to prior imitation learning methods on a set of goal-directed tasks in navigation, locomotion, and robotic manipulation, even with demonstrations that cover only a part of the states. © 2021 Neural information processing systems foundation. All rights reserved.
Other Subjects
Generalisation; Goal-directed; Imitation learning; Learning methods; Observation method; Proximity functions; Robotic manipulation; Simple++; Task information; Learning systems
Type
conference paper