Date of Award

Spring 6-8-2019

Document Type

Thesis

Degree Name

Master of Science (MS)

First Advisor

Daniel Szafir

Second Advisor

Shaun Kane

Third Advisor

Nikolaus Correll

Abstract

This dissertation addresses the topic of intuitive human-robot interaction. In particular, this dissertation takes the use case of navigating robots with gestures for example, conducts a user study with 40 participants. A taxonomy of gestures for robot navigation is gathered from this user study. A dataset of 33 videos regarding using gestures to navigate robot is annotated. In addition, feeding DiffFrame, a novel way to feed the neural network, is proposed to increase the speed of reducing cost during training. Four distinct novel deep learning neural networks, which are proposed as DiffFrameNet, are designed to recognize human's gestures from the video.

Available for download on Sunday, October 10, 2021

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