Higher Order Reasoning for Collaborative Communicationless Mobile Robot Operations

TL;DR

This paper introduces a dynamic epistemic planning framework enabling multi-robot teams to coordinate implicitly and plan over long horizons without communication, using higher-order reasoning and belief propagation.

Contribution

It presents a novel integration of higher-order epistemic reasoning with Bayesian belief updates and behavior trees for communicationless multi-robot coordination.

Findings

Reduces task completion time compared to first-order methods

Successfully demonstrated in simulations and physical experiments

Enables robust coordination without explicit communication

Abstract

In communicationless environments, multi-robot systems must operate without the constant information exchange that many coordination strategies typically assume. This paper presents a novel dynamic epistemic planning framework that enables implicit coordination and long horizon planning through higher-order reasoning among robots. With our approach, robots form and propagate higher-order belief particles, update world beliefs using Bayesian inference, and select actions via a behavior tree that anticipates teammates' likely decisions. A temporally aware Model Predictive Path Integral (MPPI) controller integrates this reasoning into low-level execution, allowing robots to plan intercepts and adapt trajectories under partial observability. The proposed framework is evaluated in both simulations and physical experiments, where it consistently reduces task completion time compared to a first-order baseline, demonstrating that epistemic logic can serve as a robust foundation for resilient coordination in communication-restricted domains.