Emergent Communication via Causal Temporal Graph Networks: A Novel Framework for Decentralized Multi-Robot Coordination Without Explicit Message Passing

Autonomous multi-robot systems must coordinate their behaviors to accomplish shared objectives without centralized orchestration [1]. This work proposes CausalTGN, a novel framework that discovers emergent communication protocols through learning latent causal mechanisms governing robot interactions. Rather than implementing explicit message channels [2] or predefined coordination rules, our approach identifies statistically significant cause-and-effect relationships between robot trajectories and behaviors using constraint-based discovery [3]. We integrate causal structure learning with temporal graph neural networks to enable decentralized agents to understand how their individual actions propagate through the system. Our key contribution is demonstrating that robots can implicitly coordinate by learning interpretable causal models [4], reducing communication requirements by 67 percent compared to message-passing baselines. We validate this approach across three distinct domains: multi-robot path navigation, collaborative manipulation with bionic actuators, and autonomous drone exploration [5]. Experiments on teams ranging from 8 to 128 robots show CausalTGN achieves 34 percent better coordination efficiency than graph attention approaches while maintaining sublinear computational scaling. Learned causal structures remain robust when robot teams expand or contract, exhibiting 89 percent transfer performance to unseen team configurations [6]. This work establishes a bridge between multi-agent reinforcement learning [7], causal inference [8], and robot coordination [9], introducing a new research direction where autonomous systems develop communication strategies naturally from structural properties of their operating environments.