Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects

抄録

We present a robotic system of picking target from a pile of objects that is capable to find and pick the target object by removing obstacles away in the appropriate order. The key idea to achieve this is segmenting instances regarding both visible and occluded masks, which we call `instance occlusion segmentation' to find which objects are occluding the target object. To achieve this, we extend existing instance segmentation model with a novel `relook' architecture, in which the model explicitly learns the inter-instance relationship. With extension to existing image synthesis, we also make the system to be capable to handle novel objects without human annotations, in consideration of the future applications like warehouse picking. The experimental results show the effectiveness of the relook architecture compared with the conventional model and image synthesis compared with the human annotations for instance occlusion segmentation. We also demonstrate the capability of our picking system for picking a target in a cluttered environment.