The Robot Swarms Are Coming (www.wsj.com)

Researchers are increasingly shifting from teaching robots to “think” like humans toward designing them to behave like ants, bees and slime molds — simple agents whose local interactions produce powerful collective intelligence. Swarm robotics embraces decentralization: many low-cost, often identical robots follow simple local rules (stigmergy, neighbor-based consensus, attraction-repulsion/velocity alignment) to generate robust, scalable emergent behaviors without centralized control. The result is systems that can self-organize for tasks such as area coverage, collective transport, exploration, and repair, and that tolerate individual failures far better than single, monolithic robots. For the AI/ML community this trend matters because it reframes autonomy as a distributed learning and control problem. Key technical directions include distributed optimization and consensus algorithms, multi-agent reinforcement learning and graph neural networks for learned local policies, sim-to-real transfer for large populations, and formal verification of emergent properties. Applications span search-and-rescue, environmental sensing, precision agriculture, warehouse logistics and planetary exploration. Challenges remain: reliable communication, safety and predictability of emergent behaviors, energy constraints, and regulatory frameworks. Integrating swarm paradigms with advances in learning, perception and verification could unlock new, cost-effective robotic capabilities — but will also demand new tools for designing, validating and governing collective AI.