Researchers at Penn’s xLAB are collaborating on a multiyear project to study how large teams of physical AI agents can cooperate, compete, and act safely outside of software and in the real world.
2 min. read
What happens when artificial intelligence agents leave the digital world and enter the physical world? Researchers at Penn’s School of Engineering and Applied Science are tackling that question through a new three-year, international collaboration on Swarm AI, led by Rahul Mangharam, professor in electrical and systems engineering and principal investigator of the Safe Autonomous Systems Lab (xLAB). The project brings together three universities to study how large teams of physical AI agents can cooperate, compete, and act safely.
Ph.D. student Hongrui Zheng (left), and Rahul Mangaharam, ESE professor and principal investigator of xLAB, with the drones used in this research.
(Image: Courtesy of Penn Engineering)
“Most of today’s AI agents live purely in software,” says Mangharam. “We’re moving toward physical AI, systems that don’t just generate answers, but act in the real world. And once AI operates in physical space, it has to deal with real constraints and real consequences.”
Unlike digital agents, physical agents must obey the laws of physics. They must avoid collisions, respect safety boundaries, and coordinate with teammates. The research centers on cooperation and coordination in adversarial games—scenarios in which teams of agents must strategize against opponents while maintaining internal cohesion.
“A key technical focus is understanding intent,” says Mangharam. “Agents must infer what other agents, human or machine, are trying to achieve and adjust accordingly. They must coordinate without centralized control and respond to dynamic, uncertain environments. This project brings together research in machine learning for multi-agent systems that use game theory to cooperate and compete.”
At large scales, this becomes an algorithmic and a systems problem: how to design distributed algorithms that scale to tens, hundreds, or even thousands of agents to make consistent, safe decisions in real time. One distinctive aspect of the project is its emphasis on neurosymbolic AI, which combines neural networks with structured, human-encoded knowledge.
“You can’t just throw AI at a problem and expect it to magically figure everything out,” says Mangharam. “There’s always human context—hard-earned domain knowledge, engineering realities, safety rules—that doesn’t live neatly in data and can’t simply be learned from scratch. If we want these systems to work in the real world, we have to teach them the fundamentals we already understand. By building those physical limits, safety boundaries and operational principles directly into the system, we develop physics-informed neural networks, or PINNs, which give AI the necessary domain knowledge on how the world works, the expectations and the lines you can’t cross.”
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