Implicit Coordination using Active Epistemic Inference

2025-01-07

AI Summary

Multi-robot coordination suffers from low efficiency under no-communication or weak-communication conditions. Method: This paper proposes an implicit coordination framework grounded in higher-order Theory of Mind (ToM), integrating active inference with hierarchical cognitive modeling for the first time. It establishes a recursively updatable belief-state mechanism enabling second-order and higher-order belief reasoning—surpassing the limitations of conventional first-order prediction. Hierarchical epistemic planning enables autonomous complex tasks (e.g., environmental monitoring) without explicit communication. Contribution/Results: Evaluated in heterogeneous robot simulations and real-world experiments, the framework achieves significantly higher task completion rates compared to greedy strategies and first-order reasoning approaches. It demonstrates robustness and effectiveness in communication-failure scenarios, validating its practical utility for decentralized multi-robot systems operating under constrained communication.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Coordination and Collaboration
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Multi-robot teamwork
• Communication constraints
• Advanced decision-making

Innovation

• Theory of Mind
• Multi-Robot Collaboration
• Hierarchical Planning