Robotics
It Takes Two to Tangram: Multi-Agent Object Centric Reinforcement Learning for Assembly
A learning approach for coordinating agents during geometric assembly
Geometric assembly requires fine spatiotemporal coordination to place parts without causing collisions or deadlocks under tight clearances.
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ArXiv CS.RO
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ArXiv CS.RO
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Assembly tasks with tight clearances demand precise coordination, since poorly timed movements can cause collisions or deadlocks. A multi-agent, object-centric reinforcement learning approach may help address these coordination challenges.
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