Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation

• URL: http://arxiv.org/abs/2011.04627v2
• Date: Fri, 13 Nov 2020 20:27:39 GMT
• Title: Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation
• Authors: Mohit Sharma, Jacky Liang, Jialiang Zhao, Alex LaGrassa, Oliver Kroemer
• Abstract summary: We propose using reinforcement learning to compose hierarchical object-centric controllers for manipulation tasks. Experiments in both simulation and real world show how the proposed approach leads to improved sample efficiency, zero-shot generalization, and simulation-to-reality transfer without fine-tuning.
• Score: 26.24940293693809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e.g., sliding an object to a goal pose while maintaining contact with a table. Individual subtasks can be achieved by task-axis controllers defined relative to the objects being manipulated, and a set of object-centric controllers can be combined in an hierarchy. In prior works, such combinations are defined manually or learned from demonstrations. By contrast, we propose using reinforcement learning to dynamically compose hierarchical object-centric controllers for manipulation tasks. Experiments in both simulation and real world show how the proposed approach leads to improved sample efficiency, zero-shot generalization to novel test environments, and simulation-to-reality transfer without fine-tuning.

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