Abstract
Grasp synthesis for 3-D deformable objects remains a little-explored topic, most works aiming to minimize deformations. However, deformations are not necessarily harmful—humans are, for example, able to exploit deformations to generate new potential grasps. How to achieve that on a robot is though an open question. This letter proposes an approach that uses object stiffness information in addition to depth images for synthesizing high-quality grasps. We achieve this by incorporating object stiffness as an additional input to a state-of-the-art deep grasp planning network. We also curate a new synthetic dataset of grasps on objects of varying stiffness using the Isaac Gym simulator for training the network. We experimentally validate and compare our proposed approach against the case where we do not incorporate object stiffness on a total of 2800 grasps in simulation and 560 grasps on a real Franka Emika Panda. The experimental results show significant improvement in grasp success rate using the proposed approach on a wide range of objects with varying shapes, sizes, and stiffnesses. Furthermore, we demonstrate that the approach can generate different grasping strategies for different stiffness values. Together, the results clearly show the value of incorporating stiffness information when grasping objects of varying stiffness. Code and video are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://irobotics.aalto.fi/defggcnn/</uri> .
Highlights
I N the last decade, advancement in robotic grasping has enabled robots to automatically grasp a never-before-seen range of objects
Our solution is based on the Generative Grasping Convolutional Neural Network 2 (GG-CNN2) a fully-convolutional network proposed by Morrision et al [31]
Can Def-GG-CNN synthesize high-quality grasps for deformable objects and would they succeed in simulation?
Summary
I N the last decade, advancement in robotic grasping has enabled robots to automatically grasp a never-before-seen range of objects. Most of the works on grasp synthesis still assume specific object properties such as uniform friction or rigidity. These assumptions do not hold for multimaterial [2] or deformable objects and can lead to unsuccessful grasping in real-world scenarios. Grasping non-rigid objects, on the other hand, is difficult because objects deform under interaction forces meaning that the 3-D contact locations depend on the forces exerted on the object. The effect of the deformation varies across deformable objects and tasks. It is essential to harness the deformation of objects when grasping
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