Samuel Li

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About

Samuel Li is a rising star in robotics and embodied AI, whose work bridges the gap between human intuition and machine capability. His primary research focuses on task-oriented grasping, action anticipation, and neuro-symbolic reasoning for assistive robotics. Li’s most notable contribution, **ShapeGrasp** (2024, 14 citations), introduces a groundbreaking zero-shot method that leverages large language models and geometric decomposition to enable robots to grasp unfamiliar objects—a critical skill for dynamic, in-home environments. This work has quickly gained traction for its elegant solution to a long-standing challenge in manipulation. Additionally, his paper *“Let Me Help You!”* (2024, 2 citations) pioneers a neuro-symbolic approach to short-context action anticipation, allowing robots to predict long-horizon tasks from minimal observations, a key step toward truly helpful in-home assistants. Li’s research is distinguished by its practical, human-centered focus, aiming to make assistive robotics accessible to the general public. With a clear trajectory toward real-world deployment, his work is shaping the future of intuitive, safe, and capable robotic helpers.