Robotic Grasping Strategies Based on Classification of Orientation State of Objects

Jui An Lin; Chen Chien Hsu

doi:10.1109/ICCE-TW52618.2021.9602960

Robotic Grasping Strategies Based on Classification of Orientation State of Objects

Jui An Lin, Chen Chien Hsu

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Robotic grasping has been studied for years, but still has lots of room for improvement due to its requirement of sufficient robustness to achieve a high success rate. This paper proposes grasping strategies that produce reliable grasping poses without human labeling. All the training and testing processes are performed in a simulation environment. We then further evaluate the quality of the grasping strategy produced by the system. High success rate result shows its potential of application in industrial production lines, helping the robot arms perform high-quality grasping with picking or similar tasks.

Original language	English
Title of host publication	2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665433280
DOIs	https://doi.org/10.1109/ICCE-TW52618.2021.9602960
Publication status	Published - 2021
Event	8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021 - Penghu, Taiwan Duration: 2021 Sept 15 → 2021 Sept 17

Publication series

Name	2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021

Conference

Conference	8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
Country/Territory	Taiwan
City	Penghu
Period	2021/09/15 → 2021/09/17

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Electrical and Electronic Engineering
Control and Optimization
Instrumentation

Access to Document

10.1109/ICCE-TW52618.2021.9602960

Cite this

Lin, J. A., & Hsu, C. C. (2021). Robotic Grasping Strategies Based on Classification of Orientation State of Objects. In 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021 (2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-TW52618.2021.9602960

Robotic Grasping Strategies Based on Classification of Orientation State of Objects. / Lin, Jui An; Hsu, Chen Chien.
2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lin, JA & Hsu, CC 2021, Robotic Grasping Strategies Based on Classification of Orientation State of Objects. in 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021. 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021, Institute of Electrical and Electronics Engineers Inc., 8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021, Penghu, Taiwan, 2021/09/15. https://doi.org/10.1109/ICCE-TW52618.2021.9602960

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AB - Robotic grasping has been studied for years, but still has lots of room for improvement due to its requirement of sufficient robustness to achieve a high success rate. This paper proposes grasping strategies that produce reliable grasping poses without human labeling. All the training and testing processes are performed in a simulation environment. We then further evaluate the quality of the grasping strategy produced by the system. High success rate result shows its potential of application in industrial production lines, helping the robot arms perform high-quality grasping with picking or similar tasks.

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Robotic Grasping Strategies Based on Classification of Orientation State of Objects

Abstract

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ASJC Scopus subject areas

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