Learning to Coordinate Manipulation Skills via Skill Behavior Diversification

Anonymous

Sep 25, 2019 ICLR 2020 Conference Blind Submission readers: everyone Show Bibtex
  • TL;DR: We propose to tackle complex tasks of multiple agents by learning composable primitive skills and coordination of the skills.
  • Abstract: When mastering a complex manipulation task, humans often decompose the task into sub-skills of their body parts, practice the sub-skills independently, and then execute the sub-skills together. Similarly, a robot with multiple end-effectors can perform a complex task by coordinating sub-skills of each end-effector. To realize temporal and behavioral coordination of skills, we propose a hierarchical framework that first individually trains sub-skills of each end-effector with skill behavior diversification, and learns to coordinate end-effectors using diverse behaviors of the skills. We demonstrate that our proposed framework is able to efficiently learn sub-skills with diverse behaviors and coordinate them to solve challenging collaborative control tasks such as picking up a long bar, placing a block inside a container while pushing the container with two robot arms, and pushing a box with two ant agents.
  • Keywords: manipulation, reinforcement learning, hierarchical reinforcement learning, modular framework, skill coordination
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