An Efficient Multi-Robot Arm Coordination Strategy for Pick-and-Place Tasks using Reinforcement Learning
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Date
2024-09-20
Publication Type
Working Paper
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yes
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Abstract
We introduce a novel strategy for multi-robot sorting of waste objects using Reinforcement Learning. Our focus lies on finding optimal picking strategies that facilitate an effective coordination of a multi-robot system, subject to maximizing the waste removal potential. We realize this by formulating the sorting problem as an OpenAI gym environment and training a neural network with a deep reinforcement learning algorithm. The objective function is set up to optimize the picking rate of the robotic system. In simulation, we draw a performance comparison to an intuitive combinatorial game theory-based approach. We show that the trained policies outperform the latter and achieve up to 16% higher picking rates. Finally, the respective algorithms are validated on a hardware setup consisting of a two-robot sorting station able to process incoming waste objects through pick-and-place operations.
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Publication status
published
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Book title
Journal / series
Volume
Pages / Article No.
2409.13511
Publisher
Cornell University
Event
Edition / version
v1
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Software
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Date collected
Date created
Subject
Robotics (cs.RO); FOS: Computer and information sciences; Robtotics
Organisational unit
09570 - Hutter, Marco / Hutter, Marco