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Robotic Motion Learning Framework to Promote Social Engagement

2017

Thesis

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This paper discusses a novel framework designed to increase human-robot interaction through robotic imitation of the user's gestures. The set up consists of a humanoid robotic agent that socializes with and play games with the user. For the experimental group, the robot also imitates one of the user's novel gestures during a play session. We hypothesize that the robot's use of imitation will increase the user's openness towards engaging with the robot. Preliminary results from a pilot study of 12 subjects are promising in that post-imitation, experimental subjects displayed a more positive emotional state, had higher instances of mood contagion towards the robot, and interpreted the robot to have a higher level of autonomy than their control group counterparts. These results point to an increased user interest in engagement fueled by personalized imitation during interaction.

Author(s): Rachael Burns
Year: 2017
Month: August

Department(s): Haptic Intelligence
Bibtex Type: Thesis (mastersthesis)

School: The George Washington University

Degree Type: Master of Science
Language: English
URL: https://search.proquest.com/docview/1953256291?pq-origsite=gscholar

BibTex

@mastersthesis{Burns_2017_MotionLearning,
  title = {Robotic Motion Learning Framework to Promote Social Engagement},
  author = {Burns, Rachael},
  school = {The George Washington University},
  month = aug,
  year = {2017},
  url = {https://search.proquest.com/docview/1953256291?pq-origsite=gscholar},
  month_numeric = {8}
}