Push-to-See: Learning Non-Prehensile Manipulation to Enhance Instance Segmentation via Deep Q-Learning

Efficient robotic manipulation of objects for sorting and searching often rely upon how well the objects are perceived and the available grasp poses. The challenge arises when the objects are irregular, have similar visual features (e.g., textureless objects) and the scene is densely cluttered. In such cases, non-prehensile manipulation (e.g., pushing) can facilitate grasping or searching by improving object perception and singulating the objects from the clutter via physical interaction. The current robotics literature in interactive segmentation focuses solely on isolated cases, where the central aim is on searching or singulating a single target object, or segmenting sparsely cluttered scenes, mainly through matching visual futures in successive scenes before and after the robotic interaction. On the other hand, in this paper, we introduce the first interactive segmentation model in the literature that can autonomously enhance the instance segmentation of such challenging scenes as a whole via optimising a Q-value function that predicts appropriate pushing actions for singulation. We achieved this by training a deep reinforcement learning model with reward signals generated by a Mask-RCNN trained solely on depth images. We evaluated our model in experiments by comparing its success on segmentation quality with a heuristic baseline, as well as the state-of-the-art Visual Pushing and Grasping (VPG) model [1]. Our model significantly outperformed both baselines in all benchmark scenarios. Furthermore, decreasing the segmentation error inherently enabled the autonomous singulation of the scene as a whole. Our evaluation experiments also serve as a benchmark for interactive segmentation research.