In industrial part kitting, 3D objects are inserted into cavities for transportation or subsequent assembly. Kitting is a critical step in industrial transportation and assembly, as kitting can decrease downstream processing and handling times and enable lower storage and shipping costs. Incorporating recent advances in deep learning and depth sensing can allow us to better automate this crucial process.

We present Kit-Net, a framework for kitting previously unseen 3D objects into cavities given depth images of both the target cavity and an object held by a gripper in an unknown initial orientation. Kit-Net uses self-supervised deep learning and data-augmentation to train a Convolutional Neural Network (CNN) to robustly estimate 3D rotations between objects and matching concave or convex cavities using a large dataset of simulated depth image pairs. Kit-Net then uses the trained CNN to implement a controller to orient and position novel objects for insertion into novel prismatic and conformal 3D cavities. Experiments in simulation suggest that Kit-Net can orient objects to have a 99% average intersection volume between the object mesh and that of the target cavity. Physical experiments with 3 industrial objects suggest that Kit-Net can successfully insert objects into cavities with a 63% success rate while a baseline which restricts itself to 2D rotations succeeds only 18% of the time.




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