End-to-End Learning to Grasp via Sampling from Object Point Clouds

TL;DR

This paper introduces L2G, an end-to-end learning approach that generates diverse robotic grasp poses from partial 3D object views, improving generalization and efficiency over previous methods.

Contribution

The paper presents a novel differentiable sampling strategy and multi-task learning framework for grasp generation from point clouds, enhancing robustness and generalization.

Findings

L2G outperforms existing methods in grasp success rate.

L2G demonstrates strong generalization to unseen objects.

The approach is efficient in training and inference.

Abstract

The ability to grasp objects is an essential skill that enables many robotic manipulation tasks. Recent works have studied point cloud-based methods for object grasping by starting from simulated datasets and have shown promising performance in real-world scenarios. Nevertheless, many of them still rely on ad-hoc geometric heuristics to generate grasp candidates, which fail to generalize to objects with significantly different shapes with respect to those observed during training. Several approaches exploit complex multi-stage learning strategies and local neighborhood feature extraction while ignoring semantic global information. Furthermore, they are inefficient in terms of number of training samples and time required for inference. In this paper, we propose an end-to-end learning solution to generate 6-DOF parallel-jaw grasps starting from the 3D partial view of the object. Our Learning to Grasp (L2G) method gathers information from the input point cloud through a new procedure that combines a differentiable sampling strategy to identify the visible contact points, with a feature encoder that leverages local and global cues. Overall, L2G is guided by a multi-task objective that generates a diverse set of grasps by optimizing contact point sampling, grasp regression, and grasp classification. With a thorough experimental analysis, we show the effectiveness of L2G as well as its robustness and generalization abilities.