Deformation-Aware Data-Driven Grasp Synthesis

Abstract

Grasp synthesis for 3D 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 paper 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 420 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 stiffness. Furthermore, we demonstrate that the approach can generate different grasping strategies for different stiffness values, such as pinching for soft objects and caging for hard objects. Together, the results clearly show the value of incorporating stiffness information when grasping objects of varying stiffness.

Multiple-Object Grasping Using a Multiple-Suction-Cup Vacuum Gripper in Cluttered Scenes

“…Another representative work is by Morrison et al [21], who generated affordance and pose maps from the rotated bounding box and designed a generative grasp CNN (GG-CNN) to directly infer the pixel-wise grasp pose and quality. To learn the grasp of a jaw gripper, many researchers [17,[22][23][24][25] later used similar methods to generate affordance map datasets from grasp configuration annotations represented by a rotated rectangle (e.g., the Cornell Grasp Dataset [15] and Jacquard Dataset [26]).…”