Interactive data collection for deep learning object detectors on humanoid robots

Elisa Maiettini, Giulia Pasquale, Lorenzo Rosasco, Lorenzo Natale

Abstract: Deep Learning (DL) methods are notoriously data hungry. Their adoption in robotics is challenging due to the cost associated with data acquisition and labeling. In this paper we focus on the problem of object detection, i.e. the simultaneous localization and recognition of objects in the scene, for which various DL architectures have been proposed in the literature. We propose to use an automatic annotation procedure, which leverages on human-robot interaction and depth-based segmentation, for the acquisition and labeling of training examples. We fine-tune the Faster R-CNN [36] network with these data acquired by the robot autonomously. We measure the performance on the same dataset and investigate the generalization abilities of the network on different settings and in absence of explicit segmentation, showing good detection performance. Experiments on the iCub humanoid robot [25] show that the proposed strategy is effective and can be used to deploy deep object detection algorithms on a robot.