Applying intrinsic motivation for visuomotor learning of robot arm motion

Shun Nishide, Harumitsu Nobuta, Hiroshi G. Okuno, Tetsuya Ogata

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

Abstract

In this paper, we present a method to apply intrinsic motivation for improving visuomotor learning of robot's arm with external object in view. Multiple Timescales Recurrent Neural Network (MTRNN) is utilized for learning the robot arm/external object dynamics. Training of MTRNN is done using the Back Propagation Through Time (BPTT) algorithm. BPTT algorithm is modified as follows. 1. Evaluate predictability of robot arm/objects using training error of MTRNN. 2. Assign a preference ratio to each object based on predictability. The preference ratio represents the weight of each object to training. Experiments were conducted using an actual robot moving the arm while a human moves his arm in the robot's camera view. The result of the experiment showed that the proposed method presents better training result of robot arm visuomotor dynamics compared to general training with BPTT.

Original languageEnglish
Title of host publication2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages364-367
Number of pages4
ISBN (Electronic)9781479953325
DOIs
Publication statusPublished - 2014
Event2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2014 - Kuala Lumpur, Malaysia
Duration: 2014 Nov 122014 Nov 15

Publication series

Name2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2014

Other

Other2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2014
CountryMalaysia
CityKuala Lumpur
Period14/11/1214/11/15

Keywords

  • Cognitive Developmental Robotics
  • Intrinsic Motivation
  • Recurrent Neural Network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction

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