Variable in-hand manipulations for tactile-driven robot hand via CNN-LSTM

Satoshi Funabashi, Shun Ogasa, Tomoki Isobe, Tetsuya Ogata, Alexander Schmitz, Tito Pradhono Tomo, Shigeki Sugano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Performing various in-hand manipulation tasks, without learning each individual task, would enable robots to act more versatile, while reducing the effort for training. However, in general it is difficult to achieve stable in-hand manipulation, because the contact state between the fingertips becomes difficult to model, especially for a robot hand with anthropomorphically shaped fingertips. Rich tactile feedback can aid the robust task execution, but on the other hand it is challenging to process high-dimensional tactile information. In the current paper we use two fingers of the Allegro hand, and each fingertip is anthropomorphically shaped and equipped not only with 6-axis force-torque (F/T) sensors, but also with uSkin tactile sensors, which provide 24 tri-axial measurements per fingertip. A convolutional neural network is used to process the high dimensional uSkin information, and a long short-term memory (LSTM) handles the time-series information. The network is trained to generate two different motions ("twist"and "push"). The desired motion is provided as a task-parameter to the network, with twist defined as -1 and push as +1. When values between -1 and +1 are used as the task parameter, the network is able to generate untrained motions in-between the two trained motions. Thereby, we can achieve multiple untrained manipulations, and can achieve robustness with high-dimensional tactile feedback.

Original languageEnglish
Title of host publication2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9472-9479
Number of pages8
ISBN (Electronic)9781728162126
DOIs
Publication statusPublished - 2020 Oct 24
Event2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 - Las Vegas, United States
Duration: 2020 Oct 242021 Jan 24

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
CountryUnited States
CityLas Vegas
Period20/10/2421/1/24

Keywords

  • Multi-in-hand manipulation
  • Neural networks
  • Tactile sensing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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