---
abstract: "In this paper, we investigated interactive learning between human subjects and robot experimentally, and its essential characteristics are examined using the dynamical systems approach. Our research concentrated on the navigation system of a specially developed humanoid robot called Robovie and seven human subjects whose eyes were covered, making them dependent on the robot for directions. We compared the usual feed-forward neural network (FFNN) without recursive connections and the recurrent neural network (RNN). Although the performances obtained with both the RNN and the FFNN improved in the early stages of learning, as the subject changed the operation by learning on its own, all performances gradually became unstable and failed. Results of a questionnaire given to the subjects confirmed that the FFNN gives better mental impressions, especially from the aspect of operability. When the robot used a consolidation-learning algorithm using the rehearsal outputs of the RNN, the performance improved even when interactive learning continued for a long time. The questionnaire results then also confirmed that the subject's mental impressions of the RNN improved significantly. The dynamical systems analysis of RNNs support these differences and also showed that the collaboration scheme was developed dynamically along with succeeding phase transitions."
altloc:
  - http://www.lucs.lu.se/ftp/pub/LUCS_Studies/LUCS101/Ogata.pdf
chapter: ~
commentary: ~
commref: ~
confdates: 'August 4-5, 2003'
conference: 'Third International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems'
confloc: 'Boston, MA, USA'
contact_email: ~
creators_id: []
creators_name:
  - family: Ogata
    given: Tetsuya
    honourific: ''
    lineage: ''
  - family: Masago
    given: Noritaka
    honourific: ''
    lineage: ''
  - family: Sugano
    given: Shigeki
    honourific: ''
    lineage: ''
  - family: Tani
    given: Jun
    honourific: ''
    lineage: ''
date: 2003
date_type: published
datestamp: 2004-02-12
department: ~
dir: disk0/00/00/33/36
edit_lock_since: ~
edit_lock_until: ~
edit_lock_user: ~
editors_id: []
editors_name:
  - family: Prince
    given: Christopher G.
    honourific: ''
    lineage: ''
  - family: Berthouze
    given: Luc
    honourific: ''
    lineage: ''
  - family: Kozima
    given: Hideki
    honourific: ''
    lineage: ''
  - family: Bullock
    given: Daniel
    honourific: ''
    lineage: ''
  - family: Stojanov
    given: Georgi
    honourific: ''
    lineage: ''
  - family: Balkenius
    given: Christian
    honourific: ''
    lineage: ''
eprint_status: archive
eprintid: 3336
fileinfo: /style/images/fileicons/application_pdf.png;/3336/1/Ogata.pdf
full_text_status: public
importid: ~
institution: ~
isbn: ~
ispublished: pub
issn: ~
item_issues_comment: []
item_issues_count: 0
item_issues_description: []
item_issues_id: []
item_issues_reported_by: []
item_issues_resolved_by: []
item_issues_status: []
item_issues_timestamp: []
item_issues_type: []
keywords: 'human-robot interaction, feed-forward neural network, recurrent neural network, dynamical systems analysis'
lastmod: 2011-03-11 08:55:25
latitude: ~
longitude: ~
metadata_visibility: show
note: ~
number: ~
pagerange: 99-106
pubdom: TRUE
publication: ~
publisher: Lund University Cognitive Studies
refereed: TRUE
referencetext: ~
relation_type: []
relation_uri: []
reportno: ~
rev_number: 12
series: ~
source: ~
status_changed: 2007-09-12 16:50:09
subjects:
  - comp-sci-mach-dynam-sys
  - comp-sci-neural-nets
  - comp-sci-robot
succeeds: ~
suggestions: ~
sword_depositor: ~
sword_slug: ~
thesistype: ~
title: Collaboration Development through Interactive Learning between Human and Robot
type: confpaper
userid: 3507
volume: 101
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