Grasping Unknown Objects in Clutter by Superquadric Representation

TL;DR

This paper introduces a fast, real-time grasping method for unknown objects in clutter using superquadric representations, symmetry assumptions, and optimized grasp planning, demonstrating superior efficiency and accuracy over learning-based methods.

Contribution

The paper presents a novel real-time grasping approach that models unknown objects with superquadrics using symmetry assumptions, enabling efficient and accurate grasp planning in cluttered environments.

Findings

Outperforms learning-based grasping algorithms in speed and accuracy.

Effective in modeling partial views of unknown objects using superquadrics.

Validated on a PR2 robot with real-time experimental results.

Abstract

In this paper, a quick and efficient method is presented for grasping unknown objects in clutter. The grasping method relies on real-time superquadric (SQ) representation of partial view objects and incomplete object modelling, well suited for unknown symmetric objects in cluttered scenarios which is followed by optimized antipodal grasping. The incomplete object models are processed through a mirroring algorithm that assumes symmetry to first create an approximate complete model and then fit for SQ representation. The grasping algorithm is designed for maximum force balance and stability, taking advantage of the quick retrieval of dimension and surface curvature information from the SQ parameters. The pose of the SQs with respect to the direction of gravity is calculated and used together with the parameters of the SQs and specification of the gripper, to select the best direction of approach and contact points. The SQ fitting method has been tested on custom datasets containing objects in isolation as well as in clutter. The grasping algorithm is evaluated on a PR2 and real time results are presented. Initial results indicate that though the method is based on simplistic shape information, it outperforms other learning based grasping algorithms that also work in clutter in terms of time-efficiency and accuracy.