Learning to Coordinate Manipulation Skills via Skill Behavior DiversificationDownload PDF

25 Sep 2019 (modified: 11 Mar 2020)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • 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 complex tasks by coordinating sub-skills of each end-effector. To realize temporal and behavioral coordination of skills, we propose a modular framework that first individually trains sub-skills of each end-effector with skill behavior diversification, and then learns to coordinate end-effectors using diverse behaviors of the skills. We demonstrate that our proposed framework is able to efficiently coordinate skills 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. Videos and code are available at https://clvrai.com/coordination
  • Keywords: reinforcement learning, hierarchical reinforcement learning, modular framework, skill coordination, bimanual manipulation
  • Code: https://github.com/clvrai/coordination
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