  Well structured visual representations can make robot learning faster and can
improve generalization. In this paper, we study how we can acquire effective
object-centric representations for robotic manipulation tasks without human
labeling by using autonomous robot interaction with the environment. Such
representation learning methods can benefit from continuous refinement of the
representation as the robot collects more experience, allowing them to scale
effectively without human intervention. Our representation learning approach is
based on object persistence: when a robot removes an object from a scene, the
representation of that scene should change according to the features of the
object that was removed. We formulate an arithmetic relationship between
feature vectors from this observation, and use it to learn a representation of
scenes and objects that can then be used to identify object instances, localize
them in the scene, and perform goal-directed grasping tasks where the robot
must retrieve commanded objects from a bin. The same grasping procedure can
also be used to automatically collect training data for our method, by
recording images of scenes, grasping and removing an object, and recording the
outcome. Our experiments demonstrate that this self-supervised approach for
tasked grasping substantially outperforms direct reinforcement learning from
images and prior representation learning methods.
