Morphology specific stepwise learning of in-hand manipulation with a four-fingered hand

Satoshi Funabashi, Alexander Schmitz, Shun Ogasa, Shigeki Sugano

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In past research, in-hand object manipulation for various sized and shaped objects has been achieved. However, the network had to be trained for each different motion. Training data takes time to acquire and increases the hardware load, thereby increasing the cost for training data. Four-fingered in-hand manipulation is especially difficult as a high number of joints need to be controlled in synchrony. This paper presents a method that reduces the required training data for in-hand manipulation with the idea of pretraining and mutual finger motions. The Allegro Hand is used with soft fingertips and integrated 6-axis F/T sensors to evaluate the proposed method. To make the network more versatile, the training data included objects of various sizes and shapes. When pretraining the network, one shot learning suffices to learn a new task; mutual finger motions can be exploited to use three-fingered pretraining data for four-fingered manipulation. Both data-sharing and weight-sharing were used and show similar results. Crucially, pretraining data from fingers with the same kinematic chain has to be used, showing the importance of morphology specific learning. Moreover, objects with untrained sizes and shapes could be manipulated.

Original languageEnglish
Article number8616845
Pages (from-to)433-441
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Volume16
Issue number1
DOIs
Publication statusPublished - 2020 Jan

Keywords

  • In-hand manipulation
  • multifingered hand
  • neural networks
  • one-shot learning
  • tactile sensing
  • weight-sharing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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