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.
LeCun,et al., Handwritten digit recognition with a back-propagation network, Advances in neural information processing systems2(1989)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.