TransSC: Transformer-based Shape Completion for Grasp Evaluation

TL;DR

This paper introduces TransSC, a transformer-based shape completion model that enhances grasp evaluation by providing detailed object geometry, leading to improved robotic grasp success rates.

Contribution

The paper presents a novel transformer-based shape completion model that outperforms existing methods and improves robotic grasping success.

Findings

TransSC outperforms existing shape completion methods in experiments.

Integration of TransSC improves grasp candidate generation.

Robots with TransSC achieve higher success in grasping randomly placed objects.

Abstract

Currently, robotic grasping methods based on sparse partial point clouds have attained a great grasping performance on various objects while they often generate wrong grasping candidates due to the lack of geometric information on the object. In this work, we propose a novel and robust shape completion model (TransSC). This model has a transformer-based encoder to explore more point-wise features and a manifold-based decoder to exploit more object details using a partial point cloud as input. Quantitative experiments verify the effectiveness of the proposed shape completion network and demonstrate it outperforms existing methods. Besides, TransSC is integrated into a grasp evaluation network to generate a set of grasp candidates. The simulation experiment shows that TransSC improves the grasping generation result compared to the existing shape completion baselines. Furthermore, our robotic experiment shows that with TransSC the robot is more successful in grasping objects that are randomly placed on a support surface.