TransSC: Transformer-based Shape Completion for Grasp Evaluation
This work addresses a domain-specific problem in robotics for improving grasp evaluation, presenting an incremental advancement over existing shape completion methods.
The paper tackles the problem of robotic grasping from sparse partial point clouds by proposing TransSC, a transformer-based shape completion model, which improves grasping performance by generating more accurate object shapes and increasing success rates in robotic experiments.
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.