A Geometric Approach for Grasping Unknown Objects With Multifingered Hands

Multifingered robotic hands offer stable grasping for a wide variety of objects, yet grasp planning with these hands is more challenging due to the high dimensionality of the search space. In this article, we propose a method for grasping unknown objects from cluttered scenes using a noisy point cloud as an input. Our approach is based on a shape complementarity metric. A fast algorithm for finding a small set of potential grasps is proposed followed by a local shape completion method to infer the occluded parts of the object. Finally, we propose an optimization-based refinement of the hand poses and finger configurations to achieve a power grasp of the target object. The proposed approach is validated extensively both on a simulated and a real world environment. We demonstrate that the proposed grasp planning algorithm produces stable grasps even in heavily dense clutter. Finally, our experiments indicate improved grasp success rate over algorithms that employ precision grasping in the same scene.