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About

Tsang-Wei Edward Lee is a leading researcher at the intersection of robotics, computer vision, and artificial intelligence, with a focus on enabling robots to operate intelligently in human-centered environments. His work spans two critical frontiers: social robot navigation and vision-language-action models. Lee's most impactful contribution is the seminal paper "RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control" (267 citations), which demonstrated how large-scale web-trained vision-language models can be integrated into end-to-end robotic control, allowing robots to generalize across tasks and exhibit emergent semantic reasoning. He also established foundational evaluation frameworks for social robot navigation through his widely cited principles and guidelines (59 citations), addressing the critical challenge of fair benchmarking in human-populated environments. Additionally, Lee developed innovative motion planning techniques, including neural collision clearance estimators (ClearanceNet) that dramatically accelerate sampling-based planning, and contributed to hierarchical navigation systems like PRM-RL for long-range indoor tasks. His recent work on PIVOT (iterative visual prompting for VLMs) and Gemini Robotics further pushes the boundaries of bringing AI into the physical world. With over 350 total citations and a growing portfolio of high-impact publications, Lee is shaping the future of capable, socially-aware robotic systems.