NDLI: Evaluating the efficacy of grasp metrics for utilization in a Gaussian Process-based grasp predictor

Content Provider	IEEE Xplore Digital Library
Author	Goins, A.K. Carpenter, R. Weng-Keen Wong Balasubramanian, R.
Copyright Year	2014
Description	Author affiliation: Sch. of Mech., Ind., & Manuf. Eng., Oregon State Univ., Corvallis, OR, USA (Goins, A.K.; Carpenter, R.; Balasubramanian, R.) \|\| Sch. of Electr. Eng. & Comput. Sci., Oregon State Univ., Corvallis, OR, USA (Weng-Keen Wong)
Abstract	With the goal of advancing the state of automatic robotic grasping, we present a novel approach that combines machine learning techniques and rigorous validation on a physical robotic platform in order to develop an algorithm that predicts the quality of a robotic grasp before execution. After collecting a large grasp sample set (522 grasps), we first conduct a thorough statistical analysis of the ability of grasp metrics that are commonly used in the robotics literature to discriminate between good and bad grasps. We then apply Principal Component Analysis and Gaussian Process algorithms on the discriminative grasp metrics to build a classifier that predicts grasp quality. The key findings are as follows: (i) several of the grasp metrics in the literature are weak predictors of grasp quality when implemented on a physical robotic platform; (ii) the Gaussian Process-based classifier significantly improves grasp prediction techniques by providing an absolute grasp quality prediction score from combining multiple grasp metrics. Specifically, the GP classifier showed a 66% percent improvement in the True Positive classification rate at a low False Positive rate of 5% when compared with classification based on thresholding of individual grasp metrics.
Starting Page	3353
Ending Page	3360
File Size	2477929
Page Count	8
File Format	PDF
e-ISBN	9781479969340
DOI	10.1109/IROS.2014.6943029
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Robots Grasping Machine learning algorithms Prediction algorithms Principal component analysis Fingers
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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