We develop solutions for collective intelligence in multi-robot systems — extending single robot capabilities through learned communication, decentralized coordination, and co-design of agents and environments.

“Session details for our three ICLR 2026 posters — diversity in cooperative MARL, remotely detectable robot policy watermarking, and hypergraph neural networks for multi-agent pathfinding. If you're attending, come say hi to Michael, Rishabh, and Matteo.”

“Teams of small robots interpreted instructions given in plain English and autonomously carried out complex missions — staying synchronised, avoiding collisions, and adapting in real time to unexpected disruptions.”

“This work bridges CONtinuous robot dynamics and scalable disCRETE search — what we call "concrete" planning. Our approach enables robots to execute agile, safe, and efficient motions even in dense environments.”

“Researchers in our lab will be showcasing a system that lets teams of robots carry out instructions given in plain English — listening to human voice, then completing their mission completely on their own.”

“The Prorok Lab has grown so much in the last 8 years and continues to be a team driven by collaboration.”

“Work that spans robot policy watermarking, higher-order interactions in multi-agent pathfinding, and the role of diversity in MARL — covering remote auditing of robot policies, hypergraph attention for dense MAPF, and when heterogeneity actually pays off.”

“Matteo's thesis on neural diversity in multi-agent learning provides a comprehensive and impactful study, demonstrating that diversity is a previously under-explored yet fundamental factor for effective collective learning in MARL.”

“Big congratulations to Rishabh Jain, Keisuke Okumura and Michael Amir on presenting "Graph Attention-Guided Search for Dense Multi-Agent Pathfinding" at AAAI 2026 — pushing forward the state of the art in scalable multi-agent coordination.”

“Grateful for a brilliant 2025 and excited for what lies ahead in 2026 — here's to continued growth, new opportunities, and building what's next.”

“Jasmine Bayrooti will be presenting work that establishes no-regret guarantees for Thompson sampling in episodic Markov Decision Processes modeled via joint multi-output Gaussian Processes.”

“Bringing together diverse teams, perspectives, and research agendas sparked exactly the kind of conversations we need as robotics moves rapidly from controlled environments into the dynamic, unpredictable real world.”

“For (arguably) the first time, our neural approach — LaGAT — clearly surpasses leading search-based planners. The key: fusing GNN-based imitation learning with the LaCAM search planner to get the best of both worlds.”

“On air we discussed how our lab is pushing the boundaries of collective intelligence in multi-robot and multi-agent systems — and what it means to take this work to a broader audience.”

“D4orm introduces a novel optimization framework for multi-robot trajectory generation. Inspired by diffusion generative models, it iteratively denoises noisy trajectories into smooth, collision-free, dynamically feasible ones — ready for real-world deployment.”

“We're looking for a motivated Postdoc to develop and deploy computer vision systems on real robots — from tracking and 3D reconstruction to visual SLAM and in-field testing with aerial and ground platforms.”

“Michael Amir presented ReCoDe at the poster session, Peter Woo showcased the Sanity quadrotor at the Open Source Hardware Workshop, and our alumnus Steven Morad delivered a spotlight talk at the RemembRL Workshop.”

“Our winning solution leverages our expertise in multi-agent systems and perception, utilizing an off-board camera network to provide the robot with real-time contextual information — demonstrating the power of collective intelligence in a dynamic, social setting.”

“The article champions a paradigm shift that lies at the heart of our lab's mission: moving away from building do-it-all robots, and instead advancing collective robotic intelligence — diverse, specialized robots that learn, adapt, and collaborate.”