2020/05/19 by Andreas Eitel, Eitel, Andreas, Nico Hauff +3
Computer Science · Engineering · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Object Detection Techniques #Robot Manipulation and Learning #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2005.09484
openalex publication_date 2020/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Instance segmentation of unknown objects from images is regarded as relevant\nfor several robot skills including grasping, tracking and object sorting.\nRecent results in computer vision have shown that large hand-labeled datasets\nenable high segmentation performance. To overcome the time-consuming process of\nmanually labeling data for new environments, we present a transfer learning\napproach for robots that learn to segment objects by interacting with their\nenvironment in a self-supervised manner. Our robot pushes unknown objects on a\ntable and uses information from optical flow to create training labels in the\nform of object masks. To achieve this, we fine-tune an existing DeepMask\nnetwork for instance segmentation on the self-labeled training data acquired by\nthe robot. We evaluate our trained network (SelfDeepMask) on a set of real\nimages showing challenging and cluttered scenes with novel objects. Here,\nSelfDeepMask outperforms the DeepMask network trained on the COCO dataset by\n9.5% in average precision. Furthermore, we combine our approach with recent\napproaches for training with noisy labels in order to better cope with induced\nlabel noise.\n