  Is it possible to learn policies for robotic assembly that can generalize to
new objects? We explore this idea in the context of the kit assembly task.
Since classic methods rely heavily on object pose estimation, they often
struggle to generalize to new objects without 3D CAD models or task-specific
training data. In this work, we propose to formulate the kit assembly task as a
shape matching problem, where the goal is to learn a shape descriptor that
establishes geometric correspondences between object surfaces and their target
placement locations from visual input. This formulation enables the model to
acquire a broader understanding of how shapes and surfaces fit together for
assembly -- allowing it to generalize to new objects and kits. To obtain
training data for our model, we present a self-supervised data-collection
pipeline that obtains ground truth object-to-placement correspondences by
disassembling complete kits. Our resulting real-world system, Form2Fit, learns
effective pick and place strategies for assembling objects into a variety of
kits -- achieving $90\%$ average success rates under different initial
conditions (e.g. varying object and kit poses), $94\%$ success under new
configurations of multiple kits, and over $86\%$ success with completely new
objects and kits.
