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About

Lucy Jackson is pioneering the future of autonomous space robotics, with a focus on making orbital infrastructure assembly and servicing both practical and cost-effective. Her research sits at the intersection of space systems engineering and artificial intelligence, where she explores how small, intelligent robots can transform the economics of space exploration. Jackson’s most influential work, “Downsizing an orbital space robot” (14 citations), provides a dynamic system-based evaluation that demonstrates how compact robots can perform complex on-orbit assembly tasks, challenging the traditional paradigm of large, monolithic spacecraft. She further advances this vision through the ORCHID framework (9 citations), which introduces a novel method for simultaneously optimizing both robotic hardware and control policies using reinforcement learning—a breakthrough that addresses the critical gap between simulation and real-world deployment. Her design of a small space robot for assembly missions (7 citations) dovetails low-cost small satellite technology with intelligent robotics, while her work on hardware-agnostic reinforcement learning (HARL-A) tackles the pervasive issue of overfitting in robotic AI. Jackson’s contributions are laying the groundwork for a new generation of agile, cost-efficient space robots that could one day assemble telescopes, refuel satellites, or build structures in orbit.