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Supporting the Education of Homebound Children Through Semi-autonomous Telepresence Robots.

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Abstract

Supporting the Education of Homebound Children Through Semi-autonomous Telepresence Robots
Supporting the Education of Homebound Children Through
Semi-autonomous Telepresence Robots
Acknowledgments
Neurorobotics Research to Improve HSR Autonomy
Supported by Toyota Motor North America.
Supported by the National Center for Research
Resources NCATS, NIH Grant TL1 TR001415.
Toyota’s Human Support Robot (HSR) and other robots
Interface
HSR Experiments with Homebound Children
Children that are homebound due to illness:
ØOften feel socially isolated and physically
segregated from their peers.
ØCan fall behind in school due to extended
absences.
Typical education services consist of only 4-5
hours of home instruction per week.
2016 US Census and NHIS data estimate the
size of the US child population who experience
significant disruption to school attendance due
to illness at 2.5 million.
Nine homebound children participated in a
Spanish lesson using the HSR in a remote
classroom.
Subjects took a survey comparing HSR features
to those in other telepresence robots.
Subjects found that HSR features improved the
telepresence class participation experience.
Subjects rated the HSR manipulation and
autonomous navigation, which is not available in
other telepresence robots, as good to excellent.
Homebound Children
We used the Toyota Human Support Robot
(HSR) to be an advanced telepresence robot
with object manipulation, autonomous
navigation, and an intuitive user interface.
We tested the telepresence HSR with
homebound children who have used other
types of telepresence robots to compare the
features and usability.
Existing telepresence robots lack many desired
features, such as mobility, autonomy, and
manipulation.
Homebound children use telepresence robots
to overcome isolation.
Classmates attribute human characteristics to
the robot
Tiffany Hwu1, Hirak J. Kashyap1, Sarah Darrow2, Douglas Moore2, Jacquelynne S. Eccles1 , Jeffrey L. Krichmar1, Veronica Newhart1
1University of California, Irvine, 2Toyota Motor North America
Background Studies
Present Study
Toyota HSR Double VGo
A brain inspired network of schema consolidation helps the
HSR predict location of objects based on context [1].
A recurrent neural model inspired by the smooth pursuit eye
movement in primates to track visual targets predictively [2].
Time: t Time: t +
!
tTarget Localization
References
1) Hwu, T. J., & Krichmar, J. L. (2018). A Neural Model of Schemas and
Memory Consolidation. bioRxiv, 434696.
2) Kashyap, H. J., Detorakis, G., Dutt, N., Krichmar, J. L., & Neftci, E. (2018). A
Recurrent Neural Network Based Model of Predictive Smooth Pursuit Eye
Movement in Primates. In Proceedings of IJCNN (pp. 5353-5360).
Object
layout
Predicted
book
location
Contacts
Email: jkrichma@uci.edu
Website: www.socsci.uci.edu/~jkrichma/CARL/
YouTube: www.youtube.com/user/cognitiveroboticsuci/
Twitter: @UCI_CARL
ResearchGate has not been able to resolve any citations for this publication.
A Neural Model of Schemas and Memory Consolidation. bioRxiv
  • T J Hwu
  • J L Krichmar
Hwu, T. J., & Krichmar, J. L. (2018). A Neural Model of Schemas and Memory Consolidation. bioRxiv, 434696.