Rethinking 6-Dof Grasp Detection: A Flexible Framework for High-Quality Grasping

TL;DR

This paper introduces FlexLoG, a versatile framework for 6-Dof robotic grasp detection that adapts to scene-level and target-oriented tasks, significantly improving grasp quality and success rates.

Contribution

The paper proposes a novel grasp-centric framework with flexible guidance and local grasp modeling, enhancing adaptability and performance over existing methods.

Findings

Achieves over 18% and 23% improvement on unseen dataset splits.

Attains a 95% success rate in real-world robotic tests.

Effectively handles both scene-level and target-oriented grasping tasks.

Abstract

Robotic grasping is a primitive skill for complex tasks and is fundamental to intelligence. For general 6-Dof grasping, most previous methods directly extract scene-level semantic or geometric information, while few of them consider the suitability for various downstream applications, such as target-oriented grasping. Addressing this issue, we rethink 6-Dof grasp detection from a grasp-centric view and propose a versatile grasp framework capable of handling both scene-level and target-oriented grasping. Our framework, FlexLoG, is composed of a Flexible Guidance Module and a Local Grasp Model. Specifically, the Flexible Guidance Module is compatible with both global (e.g., grasp heatmap) and local (e.g., visual grounding) guidance, enabling the generation of high-quality grasps across various tasks. The Local Grasp Model focuses on object-agnostic regional points and predicts grasps locally and intently. Experiment results reveal that our framework achieves over 18% and 23% improvement on unseen splits of the GraspNet-1Billion Dataset. Furthermore, real-world robotic tests in three distinct settings yield a 95% success rate.