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About

Adam Fishman is a robotics researcher whose work spans motion planning, collision avoidance, human-robot collaboration, and robot learning. He is perhaps best known for **CuRobo**, a parallelized collision-free motion generation framework that reformulates robot trajectory planning as a global optimization problem solved efficiently on massively parallel GPUs — a contribution that has garnered over 80 citations since its 2023 release and represents a significant leap forward in real-time robot manipulation. His **CabiNet** system addresses the challenge of robotic rearrangement in cluttered environments without explicit object models, leveraging procedurally generated synthetic scenes at unprecedented scale. Earlier in his career, Fishman made notable contributions to human-robot teamwork, developing trajectory optimization and collaborative behavior models that enable robots to anticipate and fluidly adapt to human actions in shared workspaces. His research on **Motion Policy Networks** further advances collision-free motion generation for unknown environments, balancing optimality with real-time performance. More recently, he has explored language-gesture-conditioned video generation for robot planning and diffusion-based motion policies, reflecting a growing interest in integrating generative AI into robotics. Across his body of work, Fishman consistently bridges theoretical rigor with practical deployability.