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Title: Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation
Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e.g., sliding an object to a goal pose while maintaining con- tact 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 with- out fine-tuning.
Authors:
; ; ; ;
Award ID(s):
1925130
Publication Date:
NSF-PAR ID:
10293204
Journal Name:
Conference on Robot Learning
Sponsoring Org:
National Science Foundation
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