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About

Laura Graesser is a robotics researcher whose work sits at the intersection of reinforcement learning, high-speed robot control, and human-robot interaction. She is best known for her sustained contributions to robotic table tennis — a demanding testbed that requires millisecond-precision perception, real-time control at 100Hz, and adaptive decision-making. Her 2020 paper introducing model-free reinforcement learning for robotic table tennis (35 citations) demonstrated that evolutionary search methods combined with CNN-based architectures could yield surprisingly capable physical policies, laying the groundwork for a research program that culminated in a landmark achievement: the first learned robot agent to reach amateur human-level performance in competitive table tennis. Along the way, Graesser tackled sim-to-real transfer in tight human-robot interaction loops and goal-conditioned control under real-world constraints. More recently, she has contributed to Gemini Robotics, exploring how large multimodal models can be grounded in physical agents, and has investigated LLMs as numerical optimizers for explainable robot self-improvement. Beyond technical research, she has championed gender diversity in robotics leadership, co-authoring a systematic study of women's representation at IEEE RAS conferences. Across roughly 100 total citations, her career reflects a commitment to pushing robots into genuinely challenging, human-facing domains.