Attention driven grasping for clearing a heap of objects

Generation of grasps for automated object manipulation in cluttered scenarios presents major challenges for various modules of the pipeline such as 2D/3D visual processing, 3D modeling, grasp hypothesis generation, grasp planning and path planning. In this paper, we present a solution framework for solving a complex instance of the problem - represented by a heap of unknown and unstructured objects in a bounded environment - in our case, a box; with the goal being removing all objects in the box using an attention driven object modeling approach to cognitive grasp planning. The focus of the algorithm delves on Grasping by Components (GBC), with a prioritization scheme derived from scene based attention and attention driven segmentation. In order to overcome the traditional challenge of segmentation performing poorly in cluttered scenes, we employ a novel active segmentation approach suited to our scenario. While the attention module helps prioritize objects in the heap and salient regions, the GBC scheme segments out parts and generates grasp hypotheses for each part. GBC is a very important component of any scalable and holistic grasping system since it abstracts point cloud object data with parametric shapes and no apriori knowledge (such as 3D models) is required. Earlier work in 3D model building (such as CAD based, simple geometries, bounding boxes, Superquadrics etc.) have depended on precise shape and pose recognition as well as exhaustive training to learn or exhaustive searching in grasp space to generate good grasp hypotheses. These methods are not scalable for real-time scenarios, complex shapes and unknown environments - key challenges in robotic grasping. In order to alleviate this concern, we present a novel parametric algorithm to estimate grasp points and approach vectors from the 3D parametric shape model, along with innovative schemes to optimize the computation of the parametric models as well as to refine the generated grasp hypotheses based on the scene information to aid path planning. We present evaluation of our complex grasping pipeline for cluttered heaps through a series of test sequences involving removal of objects from a box, along with evaluations for our attention mechanisms, active segmentation, 3D model fitting optimizations and quality of our grasp hypotheses.