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Attention-based Higher-Order Reasoning for Implicit Coordination of Multi-Robot Systems

Jonathan Reasoner, Lauren Bramblett, Nicola Bezzo

This paper presents a novel theory of mind (ToM)-based approach for implicit coordination of multi robot systems (MRS) in environments where direct communication is unavailable. The proposed approach integrates higher-order reasoning, epistemic theory, and active inference to coordinate the actions of each robot to clarify their own intentions and make them understandable to other robots. Further, to reduce the computational overhead of higher-order reasoning, we implement a large language model (LLM)-based attention selection mechanism that focuses on a subset of robots. Simulations and physical experiments demonstrate the applicability of the proposed approach with high success rates while significantly reducing computation complexity.

3 Robots Case

Full higher-order reasoning
(our approach)

Zero-order reasoning
(greedy baseline)

4 Robots Case

Full higher-order reasoning
(our approach)

LLM-based robot selection for

higher-order reasoning

(our approach)

proximity-based robot selection for

higher-order reasoning

(our approach)

7 Robots Case

Full higher-order reasoning
(our approach)

LLM-based robot selection for
higher-order reasoning
(our approach)

Zero-order reasoning
(greedy baseline)

AMR Lab
© 2025 Nicola Bezzo
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