Masters Theses

Keywords and Phrases

Human-Robot Collaboration; LSTM; Machine Learning; RNN; Robotics


"Integrating sensors that read states of the human body into everyday life is an increasing desire, especially with the rise of deep learning which requires vast stores of data to make predictions. This work explores integrating these sensors into the human experience through two methods and recording the results. The first of these methods integrates a MXene based field-effect transistor sensor for the 2019-nCov spike protein with a mobile app. This allows the user to read how saturated their breath is with Covid-19. The second method integrates 3D-printed pressure sensors, and a motion capture system, into a glove to read data on the human hand. This glove was then used in a human-robot collaboration project to teach a robot to react to a human collaborator's gestured intent after watching a collection of intentional demonstrations. This work seeks for the sensor application, human data glove, and robot-collaboration framework made in this project to be used in later scientific exploration on integrating sensors into the human experience.

Human-robot collaboration is the key emphasis of this work and was achieved through a combination of human intent prediction and robot policy encoding. Human intent prediction was achieved by a stacked LSTM neural network. This network was trained on demonstrations gathered where an individual wearing the human data glove performed an action, and a robot arm controlled by a human operator was moved through the desired trajectory in response to said action. The robot policy was encoded using a probabilistic movement primitive by learning the actions of the robot during these demonstrations. Once trained, the network could watch the actions of the human wearing the glove and respond with the appropriate robot policy with no human assistance"--Abstract, p. iii


Wu, Chenglin
Stanley, R. Joe

Committee Member(s)

Song, Yun Seong


Electrical and Computer Engineering

Degree Name

M.S. in Electrical and Computer Engineering


Missouri University of Science and Technology

Publication Date

Summer 2023


x, 94 pages

Note about bibliography

Includes_bibliographical_references_(pages 91-93)


© 2023 Adam Sawyer, All Rights Reserved

Document Type

Thesis - Open Access

File Type




Thesis Number

T 12298

Electronic OCLC #