An offline learning of behavior correction policy for vision-based robotic manipulation
Qingxiuxiong Dong,Toshimitsu Kaneko,Masashi Sugiyama,Qingxiuxiong Dong,Toshimitsu Kaneko,Masashi Sugiyama
Offline learning usually requires a large dataset for training. In this paper, we focus on vision-based robotic manipulation tasks and utilize certain task properties to achieve offline learning with a small dataset. We propose a two-stage agent consisting of a tentative decision stage and a correction stage, where the tentative decision stage determines a tentative action from the original camera...
