53 citations · 68 across the 8 of their papers we have counts for
4 papers · 1 filter
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment
Mingxin Yu, Lin Shao, Zhehuan Chen +4
Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape. In this paper, we develop a robotic assembly sim…
SAGCI-System: Towards Sample-Efficient, Generalizable, Compositional, and Incremental Robot Learning
Jun Lv, Qiaojun Yu, Lin Shao +3
Building general-purpose robots to perform a diverse range of tasks in a large variety of environments in the physical world at the human level is extremely challenging. It require…
Learning to Regrasp by Learning to Place
Shuo Cheng, Kaichun Mo, Lin Shao
In this paper, we explore whether a robot can learn to regrasp a diverse set of objects to achieve various desired grasp poses. Regrasping is needed whenever a robot's current gras…
OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation
Yifan You, Lin Shao, Toki Migimatsu +1
In this paper, we explore whether a robot can learn to hang arbitrary objects onto a diverse set of supporting items such as racks or hooks. Endowing robots with such an ability ha…