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Grasp-Pose Prediction for Hand-Held Objects

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Emerging Technology in Modelling and Graphics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 937))

Abstract

Robotic grasp prediction of small objects is a recent challenge, incorporating concepts such as depth perception, geometric matching, deep feature-engineering, etc. However, the entirety of the existing literature focuses on the usage of simple two-fingered robotic hands to grasp handheld objects. Our paper promotes the use of five-fingered humanoid hands for better grasping and proposes a novel hand grasp-pose prediction model that learns features present in small objects to predict appropriate poses that can be assumed by the fingers of the robotic hand to facilitate complex grasp prediction, as observed in humans. We make use of the coil-100 dataset that comprises images of various handheld objects and employ a supervised learning mechanism to perform multi-label classification over a set of eight selected grasp-poses.

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Author information

Authors and Affiliations

  1. Institute of Engineering and Management, Salt Lake Electronics Complex, Gurukul Y-12, Sector V, Kolkata, 700091, India

    Abhirup Das, Ayon Chattopadhyay, Firdosh Alia & Juhi Kumari

Authors
  1. Abhirup Das
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  2. Ayon Chattopadhyay
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  3. Firdosh Alia
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  4. Juhi Kumari
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Corresponding author

Correspondence to Abhirup Das .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Jyotsna Kumar Mandal

  2. Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, West Bengal, India

    Debika Bhattacharya

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Das, A., Chattopadhyay, A., Alia, F., Kumari, J. (2020). Grasp-Pose Prediction for Hand-Held Objects. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_19

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  • DOI: https://doi.org/10.1007/978-981-13-7403-6_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7402-9

  • Online ISBN: 978-981-13-7403-6

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