A closed-loop multi-agent LLM framework enables heterogeneous robots to collaboratively manipulate objects by decomposing tasks, grounding actions via visual tools, and recovering from execution failures hierarchically.
arXiv preprint arXiv:2507.18262 (2025)
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A multi-agent large-model framework (Active Spatial Brain + Generalizable Action Cerebellum) enables spatial-aware humanoid whole-body manipulation without task-specific real-robot data.
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A Closed-Loop Multi-Agent Framework for Robust Multi-Robot Manipulation
A closed-loop multi-agent LLM framework enables heterogeneous robots to collaboratively manipulate objects by decomposing tasks, grounding actions via visual tools, and recovering from execution failures hierarchically.
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A multi-agent large-model framework (Active Spatial Brain + Generalizable Action Cerebellum) enables spatial-aware humanoid whole-body manipulation without task-specific real-robot data.