Tool-Use Model Considering Tool Selection by a Robot Using Deep Learning

Namiko Saito, Kitae Kim, Shingo Murata, Tetsuya Ogata, Shigeki Sugano

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

Abstract / Description of output

We propose a tool-use model that can select tools that require neither labeling nor modeling of the environment and actions. With this model, a robot can choose a tool by itself and perform the operation that matches a human command and the environmental situation. To realize this, we use deep learning to train sensory motor data recorded during tool selection and tool use as experienced by a robot. The experience includes two types of selection, namely according to function and according to size, thereby allowing the robot to handle both situations. For evaluation, the robot is required to generate motion either in an untrained situation or using an untrained tool. We confirm that the robot can choose and use a tool that is suitable for achieving the target task.
Original languageEnglish
Title of host publication2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)
PublisherIEEE
Pages270-276
Number of pages7
ISBN (Print)9781538672846
DOIs
Publication statusPublished - 9 Nov 2018
Event2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids) - Beijing, China
Duration: 6 Nov 20189 Nov 2018

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
PublisherIEEE
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580

Conference

Conference2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)
Period6/11/189/11/18

Keywords / Materials (for Non-textual outputs)

  • tools
  • task analysis
  • robot sensing systems
  • feature extraction
  • neurons
  • training

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