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Publication Date

2025-5

First Advisor

Kevin Huang

Document Type

Honors Project

Degree Name

Bachelor of Arts

Department

Engineering

Keywords

deep learning, authentication, robotics

Abstract

As robotic systems become increasingly integrated into sensitive domains such as surgery, teleoperation, and industrial automation, ensuring secure and continuous user authentication has become a critical concern. This thesis presents a framework based on machine learning to identify and authenticate users through behavioral patterns captured in teleoperated robot control sequences. The RoboTurk dataset, which comprises end-effector motion data over time from a 7-degree-of-freedom robotic arm, is utilized to train Long Short-Term Memory (LSTM) networks capable of modeling user-specific motion signatures. The sliding window technique is applied to segment sequences into fixed-length inputs, enabling the realtime analysis and avoiding the limitations of zero-padding and interpolation methods that require access to complete trajectories. The proposed approach achieves an average classification accuracy of 93.4% under k-fold cross-validation, demonstrating its effectiveness in distinguishing between up to 40 users. The method generally offers a scalable and real-time compatible solution for secure user identification in human-robot interaction systems.

Rights

©2025 Sylvia Tan. Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.

Language

English

Comments

[3], iv, 75 pages) : color illustrations, charts. Includes bibliographical references.

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