Publications

You can also find my articles on my Google Scholar profile.

Master's Thesis

Stiffness Mapping of Deformable Objects through Supervised Embedding and Gaussian Process Regression

Published in Carnegie Mellon University, 2022

This Master's thesis develops a novel approach for mapping the stiffness properties of deformable objects by combining supervised embedding techniques with Gaussian process regression, enabling robots to understand and predict the mechanical properties of soft materials through tactile interaction.

Recommended citation: E. Harber. (2022). "Stiffness Mapping of Deformable Objects through Supervised Embedding and Gaussian Process Regression." Master's thesis, Carnegie Mellon University, Pittsburgh, PA.
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Patents

Sensing Device

Published in World Intellectual Property Organization (WIPO), 2022

This international patent describes a novel sensing device technology with applications in tactile sensing and robotic perception. The patent represents the intellectual property protection for innovative sensor design and manufacturing approaches developed through collaborative research.

Recommended citation: L. Li, E. Schindewolf, E. Harber, and H. Choset. (2022). "Sensing Device." Patent WO2022093771A1, World Intellectual Property Organization.
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Conference Papers

Optistrain: A vision- and microfluidics-based tactile sensor with high spatial and temporal resolution

Published in IEEE World Haptics Conference, 2025

This paper presents Optistrain, a novel tactile sensor that combines vision and microfluidics technologies to achieve high spatial and temporal resolution for haptic sensing applications.

Recommended citation: E. Harber, C. P. Johnson, A. Liebman, A. Psychoyos, M. Whidby, S.-M. Kang, J. Peñaloza, A. T. Bender, J. D. Posner, and V. J. Santos. (2025). "Optistrain: A vision- and microfluidics-based tactile sensor with high spatial and temporal resolution." IEEE World Haptics Conference. Work in Progress.

NeuroReality™: A Data Distribution Service-based Inter-Process Communication Middleware

Published in 2024 IEEE Conference on Telepresence, 2024

This paper introduces NeuroReality™, a DDS-based publish-subscribe middleware designed for distributed real-time systems in telepresence and teleoperation applications, offering low-latency, high reliability, and high throughput communication that outperforms traditional server-client architectures.

Recommended citation: S. Asjad, E. Harber, V. Santos, and D. Tyler. (2024). "NeuroReality™: A Data Distribution Service-based Inter-Process Communication Middleware." 2024 IEEE Conference on Telepresence. pp. 168-171.
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Design of a Biomimetic Tactile Sensor for Material Classification

Published in 2022 International Conference on Robotics and Automation (ICRA), 2022

This paper presents the design of a biomimetic tactile sensor inspired by biological sensory systems, capable of classifying different materials through tactile interaction. The sensor demonstrates improved material discrimination capabilities compared to traditional approaches.

Recommended citation: K. Dai, X. Wang, A. M. Rojas, E. Harber, Y. Tian, N. Paiva, J. Gnehm, E. Schindewolf, H. Choset, V. A. Webster-Wood, and L. Li. (2022). "Design of a Biomimetic Tactile Sensor for Material Classification." 2022 International Conference on Robotics and Automation (ICRA). pp. 10774-10780.
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Toward Robotically Automated Femoral Vascular Access

Published in 2021 International Symposium on Medical Robotics (ISMR), 2021

This paper presents research toward developing a robotic system for automated femoral vascular access, addressing critical challenges in emergency medicine and surgical procedures through advanced robotics, computer vision, and medical device integration.

Recommended citation: N. Zevallos*, E. Harber*, Abhimanyu, K. Patel, Y. Gu, K. Sladick, F. Guyette, L. Weiss, M. R. Pinsky, H. Gomez, J. Galeotti, and H. Choset. (2021). "Toward Robotically Automated Femoral Vascular Access." 2021 International Symposium on Medical Robotics (ISMR). pp. 1-7.
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A Tunable Magnet-Based Tactile Sensor Framework

Published in 2020 IEEE SENSORS, 2020

This paper presents a novel tunable magnet-based tactile sensor framework that leverages magnetic field sensing for tactile perception. The system offers adjustable sensitivity and robust performance for robotic and biomedical sensing applications.

Recommended citation: E. Harber, E. Schindewolf, V. Webster-Wood, H. Choset, and L. Li. (2020). "A Tunable Magnet-Based Tactile Sensor Framework." 2020 IEEE SENSORS. pp. 1-4.
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Evan Harber