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You Liang Tan is a leading researcher in robotics, with key contributions spanning generalist robot policies, sample-efficient reinforcement learning, and human-robot interaction for healthcare. His most impactful work, "Octo: An Open-Source Generalist Robot Policy" (2024), has garnered 66 citations and introduces a large, pretrained policy that can be fine-tuned with minimal in-domain data, enabling broad generalization across diverse robotic platforms. Tan also developed "SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning" (2024, 31 citations), which advances real-world training with complex image observations and auxiliary data. His innovative "ForceSight" system (2024) combines text-guided mobile manipulation with visual-force goals, while "GR00T N1" (2025) extends his work to open foundation models for generalist humanoid robots. Notably, Tan applies robotics to healthcare, creating "Stretch with Stretch" (2024), a mobile manipulator that leads physical therapy exercise games for Parkinson’s disease patients, and collaborating with exercise specialists to evaluate robot-led rehabilitation. With over 120 total citations and a focus on open-source solutions, Tan’s work bridges cutting-edge AI and real-world impact, making him a pivotal figure in advancing accessible, generalist robotics.