Using Geometry to Detect Grasps in 3D Point Clouds

TL;DR

This paper introduces a geometry-based method for detecting grasp points on 3D objects in cluttered environments, achieving high success rates by leveraging geometric conditions and antipodal grasp classification.

Contribution

The paper presents a novel geometric approach that efficiently samples and classifies grasp hypotheses, significantly improving grasp detection accuracy in cluttered scenes.

Findings

Achieves 88% success rate on isolated objects

Achieves 73% success rate in dense clutter

Uses geometric conditions for high-quality grasp hypothesis sampling

Abstract

This paper proposes a new approach to detecting grasp points on novel objects presented in clutter. The input to our algorithm is a point cloud and the geometric parameters of the robot hand. The output is a set of hand configurations that are expected to be good grasps. Our key idea is to use knowledge of the geometry of a good grasp to improve detection. First, we use a geometrically necessary condition to sample a large set of high quality grasp hypotheses. We were surprised to find that using simple geometric conditions for detection can result in a relatively high grasp success rate. Second, we use the notion of an antipodal grasp (a standard characterization of a good two fingered grasp) to help us classify these grasp hypotheses. In particular, we generate a large automatically labeled training set that gives us high classification accuracy. Overall, our method achieves an average grasp success rate of 88% when grasping novels objects presented in isolation and an average success rate of 73% when grasping novel objects presented in dense clutter. This system is available as a ROS package at http://wiki.ros.org/agile_grasp.