Implicit Coordination using Active Epistemic Inference for Multi-Robot Systems

TL;DR

This paper introduces a novel multi-robot coordination framework that employs higher-order epistemic reasoning and active inference to operate effectively under communication constraints, outperforming existing methods.

Contribution

It presents a new framework using Theory of Mind and hierarchical epistemic planning for multi-robot coordination without reliable communication.

Findings

Outperforms greedy and first-order reasoning approaches

Validated through simulations and real experiments

Effective in scenarios with communication failures

Abstract

A Multi-robot system (MRS) provides significant advantages for intricate tasks such as environmental monitoring, underwater inspections, and space missions. However, addressing potential communication failures or the lack of communication infrastructure in these fields remains a challenge. A significant portion of MRS research presumes that the system can maintain communication with proximity constraints, but this approach does not solve situations where communication is either non-existent, unreliable, or poses a security risk. Some approaches tackle this issue using predictions about other robots while not communicating, but these methods generally only permit agents to utilize first-order reasoning, which involves reasoning based purely on their own observations. In contrast, to deal with this problem, our proposed framework utilizes Theory of Mind (ToM), employing higher-order reasoning by shifting a robot's perspective to reason about a belief of others observations. Our approach has two main phases: i) an efficient runtime plan adaptation using active inference to signal intentions and reason about a robot's own belief and the beliefs of others in the system, and ii) a hierarchical epistemic planning framework to iteratively reason about the current MRS mission state. The proposed framework outperforms greedy and first-order reasoning approaches and is validated using simulations and experiments with heterogeneous robotic systems.