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shape matching plays an important role in many manipulation tasks such as kid assembly in fact the shape of an object often matches the shape of the corresponding space in its placement location and understanding this relationship is what allows us to seamlessly assemble objects in our everyday interactions unfortunately this concept is often overlooked in many modern assembly systems which typically rely on lots of prior knowledge like 3d models to estimate object poses this hinders their adaptability to tasks with new objects they have never seen before if frobots could instead generally learn how things fit together would it enable them to immediately generalize to new objects in this paper we present an approach to learning generalizable policies for robotic kit assembly through shape matching which is entirely self supervise here is a demonstration of our system in action it learns to use visual and geometric use to establish correspondences between objects and their target place