Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data
Mohi Khansari,Daniel Kappler,Jianlan Luo,Jeff Bingham,Mrinal Kalakrishnan,Mohi Khansari,Daniel Kappler,Jianlan Luo,Jeff Bingham,Mrinal Kalakrishnan
This paper introduces Action Image, a new grasp proposal representation that allows learning an end-to-end deep-grasping policy. Our model achieves 84% grasp success on 172 real world objects while being trained only in simulation on 48 objects with just naive domain randomization. Similar to computer vision problems, such as object detection, Action Image builds on the idea that object features a...
