GraspCaps: A Capsule Network Approach for Familiar 6DoF Object Grasping

TL;DR

This paper introduces GraspCaps, a Capsule Network-based method for generating precise 6DoF grasp configurations for familiar objects, demonstrating improved success rates in real and simulated environments.

Contribution

The paper presents a novel Capsule Network architecture for 6DoF grasping and a new dataset generation method using simulated annealing.

Findings

Significantly higher grasp success rates compared to baselines.

Effective learning of object-specific grasping strategies.

Robust performance in challenging real and simulated scenarios.

Abstract

As robots become more widely available outside industrial settings, the need for reliable object grasping and manipulation is increasing. In such environments, robots must be able to grasp and manipulate novel objects in various situations. This paper presents GraspCaps, a novel architecture based on Capsule Networks for generating per-point 6D grasp configurations for familiar objects. GraspCaps extracts a rich feature vector of the objects present in the point cloud input, which is then used to generate per-point grasp vectors. This approach allows the network to learn specific grasping strategies for each object category. In addition to GraspCaps, the paper also presents a method for generating a large object-grasping dataset using simulated annealing. The obtained dataset is then used to train the GraspCaps network. Through extensive experiments, we evaluate the performance of the proposed approach, particularly in terms of the success rate of grasping familiar objects in challenging real and simulated scenarios. The experimental results showed that the overall object-grasping performance of the proposed approach is significantly better than the selected baseline. This superior performance highlights the effectiveness of the GraspCaps in achieving successful object grasping across various scenarios.