Abstract

We present a novel grasping approach for unknown stacked ob jects using RGB-D images of highly complex real-world scenes. Specif- ically, we propose a novel 3D segmentation algorithm to generate an effi- cient representation of the scene into segmented surfaces (known as sur- fels) and ob jects. Based on this representation, we next propose a novel grasp selection algorithm which generates potential grasp hypotheses and automatically selects the most appropriate grasp without requiring any prior information of the ob jects or the scene. We tested our algorithms in real-world scenarios using live video streams from Kinect and publicly available RGB-D ob ject datasets. Our experimental results show that both our proposed segmentation and grasp selection algorithms consis- tently perform superior compared to the state-of-the-art methods.