Lin SHAO

Lin SHAO is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research interests lie at the intersection of Robotics and Artificial Intelligence, focusing on robotic perception and manipulation. His group develops algorithms and systems to enable robots to perform diverse tasks in various environments. He is a co-chair of the Technical Committee on Robot Learning in the IEEE Robotics and Automation Society and serves as an Associated Editor at ICRA 2024. His work received the Best System Paper Award finalist at RSS 2023. He received his Ph.D. from Stanford University in 2021, advised by Jeannette Bohg and co-advised by Leonidas J. Guibas. He also holds an M.S. from Stanford University (2017) and a B.S. in Geochemistry from Nanjing University (2014).

Lin Shao is a researcher at the National University of Singapore focusing on robotics, cognitive science, reinforcement learning, concept learning, and computer vision. Their work explores the integration of cognitive principles into robotic systems, emphasizing concept learning and manipulation through human demonstrations and instructions. Key projects include developing models for robotic grasping, 3D shape reconstruction, and motion-based object segmentation. Lin's research also addresses the challenges of real-time robot manipulation using equivariant representations and differentiable physics-based simulations. Additionally, they investigate vision-language-action models and multi-robot systems with LLM agents, aiming to enhance sample efficiency and generalizability in robotic learning.