Development of a robust cascaded architecture for intelligent robot grasping using limited labelled data

TL;DR

This paper introduces a semi-supervised learning architecture based on VQVAE and GGCNN2 for robot grasping, achieving high accuracy with limited labeled data and validated through real robot experiments.

Contribution

It presents a novel semi-supervised learning model that improves robot grasping performance with limited labeled data, combining representation learning and GGCNN2 architecture.

Findings

6% performance improvement over state-of-the-art models

Significant better grasping in cluttered environments

Validated with hardware experiments on Baxter robot

Abstract

Grasping objects intelligently is a challenging task even for humans and we spend a considerable amount of time during our childhood to learn how to grasp objects correctly. In the case of robots, we can not afford to spend that much time on making it to learn how to grasp objects effectively. Therefore, in the present research we propose an efficient learning architecture based on VQVAE so that robots can be taught with sufficient data corresponding to correct grasping. However, getting sufficient labelled data is extremely difficult in the robot grasping domain. To help solve this problem, a semi-supervised learning based model which has much more generalization capability even with limited labelled data set, has been investigated. Its performance shows 6\% improvement when compared with existing state-of-the-art models including our earlier model. During experimentation, It has been observed that our proposed model, RGGCNN2, performs significantly better, both in grasping isolated objects as well as objects in a cluttered environment, compared to the existing approaches which do not use unlabelled data for generating grasping rectangles. To the best of our knowledge, developing an intelligent robot grasping model (based on semi-supervised learning) trained through representation learning and exploiting the high-quality learning ability of GGCNN2 architecture with the limited number of labelled dataset together with the learned latent embeddings, can be used as a de-facto training method which has been established and also validated in this paper through rigorous hardware experimentations using Baxter (Anukul) research robot.