Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptors
Hoang–Giang Cao,Weihao Zeng,I–Chen Wu,Hoang–Giang Cao,Weihao Zeng,I–Chen Wu
Picking cluttered general objects is a challenging task due to the complex geometries and various stacking configurations. Many prior works utilize pose estimation for picking, but pose estimation is difficult on cluttered objects. In this paper, we propose Cluttered Objects Descriptors (CODs), a dense cluttered objects descriptor which can represent rich object structures, and use the pre-trained...
