Grasping Novel Objects by Semi-supervised Domain Adaptation.

Learning-based robot arm grasping approach attracts increasing interests recently. The algorithm needs to accurately locate the grasping point and angle. Existing methods usually require large amount of training data from physical robotic trial or synthetic samples from simulation. The system can show promising result with pre-defined objects, but the performance may degrade for novel objects without annotation. Inspired by the fact that we can usually have a large set of pre-collected training data from external source, but only a small quantity of data for the target novel objects, we introduce a new deep adaptation learning approach that is able to transfer the grasping knowledge from the source domain with known objects to target domain with novel objects. Partially or totally not labeled target domain data can be employed in our method. A label propagation scheme is further utilized for domain transfer learning. Experiments on a Baxter robot demonstrate substantial grasp accuracy improvement with the proposed approach even the target objects are totally unlabeled.