arXiv:2509.24575v2 Announce Type: replace-cross Abstract: This paper presents a framework to prompt multi-robot teams with high-level tasks using natural language expressions. Our objective is to use the reasoning capabilities of language models in understanding and decomposing multi-robot collaboration and decision-making tasks, but in settings where such models cannot be called at deployment time. However, it is hard to specify the behavior of an individual robot from a team instruction, and have it continuously adapt to actions from other robots. This necessitates a framework with the repre
The proliferation of advanced language models and the increasing sophistication of robotic hardware are converging, making natural language interfaces for robotic teams a logical next step.
This breakthrough addresses a critical bottleneck in deploying multi-robot systems by enabling intuitive high-level task specification, dramatically lowering the barrier to entry for complex automation.
Robot teams can now be commanded with natural language, shifting the programming paradigm from low-level coding to high-level intent-based instructions, even in environments with limited computational resources.
- · Robotics companies
- · Logistics and manufacturing
- · Defence sectors
- · AI software developers
- · Traditional industrial programming firms
- · Companies reliant on single-robot solutions
Further acceleration in the adoption and deployment of multi-robot systems across various industries.
Increased demand for robust and decentralized AI reasoning capabilities at the edge for autonomous systems.
The development of highly adaptive and self-organizing robotic workforces capable of performing complex, dynamic tasks with minimal human oversight.
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