A graph-neural-network policy trained with reinforcement learning solves joint task allocation, scheduling, and motion planning for multi-robot reaching, scaling to eight arms and 40 tasks with zero-shot generalization.
Bradbury,et al., JAX: composable transformations of Python+NumPy programs (2018)
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RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning
A graph-neural-network policy trained with reinforcement learning solves joint task allocation, scheduling, and motion planning for multi-robot reaching, scaling to eight arms and 40 tasks with zero-shot generalization.