Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes

TL;DR

Contact-GraspNet is an end-to-end neural network that efficiently generates 6-DoF grasps directly from depth data, significantly improving success rates in cluttered scenes compared to previous methods.

Contribution

It introduces a novel 4-DoF grasp representation rooted in point cloud data, enabling efficient learning and generalization for robotic grasping in cluttered environments.

Findings

Achieves over 90% success rate in robotic grasping of unseen objects.

Reduces failure rate by half compared to recent state-of-the-art methods.

Trained on 17 million simulated grasps, generalizes well to real-world data.

Abstract

Grasping unseen objects in unconstrained, cluttered environments is an essential skill for autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning, existing approaches often consist of complex sequential pipelines that possess several potential failure points and run-times unsuitable for closed-loop grasping. Therefore, we propose an end-to-end network that efficiently generates a distribution of 6-DoF parallel-jaw grasps directly from a depth recording of a scene. Our novel grasp representation treats 3D points of the recorded point cloud as potential grasp contacts. By rooting the full 6-DoF grasp pose and width in the observed point cloud, we can reduce the dimensionality of our grasp representation to 4-DoF which greatly facilitates the learning process. Our class-agnostic approach is trained on 17 million simulated grasps and generalizes well to real world sensor data. In a robotic grasping study of unseen objects in structured clutter we achieve over 90% success rate, cutting the failure rate in half compared to a recent state-of-the-art method.