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About

Jessica Lam is a robotics researcher whose work bridges the gap between cutting-edge reinforcement learning and practical, real-world deployment. Her primary research areas include deep reinforcement learning for robotic manipulation, autonomous systems, and marine robotics. Lam’s most impactful contribution is her pioneering work on scaling deep RL to operate a fleet of mobile manipulators that sort recyclables and trash in office buildings—a project that tackles the immense challenge of deploying learned policies in dynamic, unstructured environments. This research, which has garnered 15 citations, demonstrates not only effective training algorithms but also the critical infrastructure needed to bootstrap and maintain real-world robotic systems. In addition, Lam has contributed to the characterization and testing of the Blue Robotics T200 Thruster, the most widely adopted thruster in marine robotics, with her work cited 8 times. This dual expertise in both terrestrial and aquatic robotic systems highlights her versatility as an engineer. Through her research, Lam is helping to make autonomous waste sorting a scalable reality, while also advancing the foundational tools that power thousands of marine vehicles worldwide.