COM2-04-29
651 62908

https://linsats.github.io

SHAO Lin

Assistant Professor

  • Ph.D. (Stanford University, 2021)
  • M.S. (Stanford University, 2017)
  • B.S. (Nanjing University, 2014)

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. His long-term goal is to build general-purpose robotic systems that intelligently perform a diverse range of tasks in a large variety of environments in the physical world. Specifically, his group is interested in developing algorithms and systems to provide robots with the abilities of perception and manipulation. He is a co-chair of the Technical Committee on Robot Learning in the IEEE Robotics and Automation Society. Previously, he received his Ph.D. at Stanford University and his B.S. at Nanjing University.

RESEARCH AREAS

Artificial Intelligence
  • Decision Making & Planning
  • Machine Learning
  • Robotics

RESEARCH INTERESTS

  • Robotic Perception and Manipulation

  • Reinforcement Learning

  • Differential Physics Simulation

RESEARCH PROJECTS

RESEARCH GROUPS

TEACHING INNOVATIONS

SELECTED PUBLICATIONS

  • SAGCI-System: Towards Sample-Efficient, Generalizable, Compositional, and Incremental Robot Learning. IEEE International Conference on Robotics and Automation (ICRA), 2022. Jun Lv*, Qiaojun Yu*, Lin Shao*, Wenhai Liu, Wenqiang Xu, Cewu Lu.
  • Concept2Robot: Learning Manipulation Concepts from Instructions and Human Demonstrations. The International Journal of Robotics Research (IJRR), Special Issue RSS 2020, 2021. Lin Shao, Toki Migimatsu, Qiang Zhang, Karen Yang, Jeannette Bohg.
  • UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands. IEEE Robotics and Automation Letters (RA-L), 2020. Lin Shao, Fabio Ferreira*, Mikael Jorda*, Varun Nambiar*, Jianlan Luo, Juan Aparicio Ojea, Oussama Khatib, Jeannette Bohg.
  • Motion-based Object Segmentation based on Dense RGB-D Scene Flow. IEEE Robotics and Automation Letters (RA-L), 2018. Lin Shao, Parth Shah*, Vikranth Dwaracherla*, Jeannette Bohg.
  • Learning to Regrasp by Learning to Place. Conference on Robot Learning (CoRL), 2021. Shuo Chen, Kaichun Mo, Lin Shao.
  • GRAC: Self-Guided and Self-Regularized Actor-Critic. Conference on Robot Learning (CoRL), 2021. Lin Shao, Yifan You, Mengyuan Yan, Shenli Yuan, Qingyun Sun, Jeannette Bohg.
  • OmniHang: Learning to Hang Arbitrary Objects Using Contact Point Correspondences and Neural Collision Estimation. IEEE International Conference on Robotics and Automation (ICRA), 2021. Yifan You*, Lin Shao*, Toki Migimatsu, Jeannette Bohg.
  • Generative 3D Part Assembly via Dynamic Graph Learning. Conference on Neural Information Processing Systems (NeurIPS), 2020. Jialei Huang*, Guanqi Zhan*, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas J. Guibas, Hao Dong.
  • Learning 3D Part Assembly from a Single Image. European Conference on Computer Vision (ECCV), 2020. Yichen Li*, Kaichun Mo*, Lin Shao, Minhyuk Sung, Leonidas J. Guibas.
  • Design and Control of Roller Grasper V2 for In-Hand Manipulation. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020. Shenli Yuan, Lin Shao, Connor L. Yako, Alex Gruebele, J. Kenneth Salisbury.
  • Learning to Scaffold the Development of Robotic Manipulation Skills. IEEE International Conference on Robotics and Automation (ICRA), 2020. Lin Shao, Toki Migimatsu, Jeannette Bohg.

AWARDS & HONOURS

MODULES TAUGHT

CS6284
Topics in Computer Science: Advanced Topics in Reinforcement Learning