Dr. Zhegong SHANGGUAN - 上官哲公

About Me

I am a Research Associate/Postdoctoral Researcher at the University of Manchester, specializing in cognitive robotics, human-robot interaction, and psychology. My research explores the development of human cognition, employing cognitive and developmental robotics approaches to investigate this process. By integrating these methodologies, I aim to enhance robots with advanced cognitive capabilities and expressive emotional interactions.

Currently, I am working on the ERC-funded e-TALK project under the supervision of Prof. Angelo Cangelosi at the Cognitive Robotics Lab, University of Manchester.

I obtained my Ph.D. in Computer Science from École Nationale Supérieure de Techniques Avancées (ENSTA), Institut Polytechnique de Paris, where I conducted research at the U2IS (L'unité d'Informatique et d'Ingénierie des Systèmes) Laboratory under the supervision of Prof. Adriana Tapus. My doctoral work focused on social robotics and trustworthy human-vehicle interaction.

In addition to my research, I serve as a reviewer for leading journals and conferences, including The International Journal of Robotics Research (IJRR), the International Journal of Human-Computer Interaction (IJHCI), CSCW, IROS, and ICRA. I am also a member of the Editorial Board for Embodied Intelligence and Robotics and a committee member of the ECSCW 2025 Exploration Session.

Research Interests

Cognitive Robotics
Human-Robot Interaction
Cognitive Psychology
Artificial Intelligence
Social Robotics
Machine Learning for HRI

Selected Publications

Factors Influencing Emotional Driving: Examining the Impact of Arousal on the Interplay Between Age, Personality, and Driving Behaviors

Shangguan, Z., Han, X., Mrhasli, Y.E., Lyu, N., Tapus, A.

Frontiers in Psychology, 2025

An investigation into how emotional arousal affects driving behavior across different age groups and personality types.

PDF DOI

Using a Pneumatic Tactile Steering Wheel to Enhance the Multi-Modal Takeover Request In Smart Vehicle

Shangguan, Z., Liu, Y., Song, L., Li, T., Tapus, A.

International Conference on Social Robotics, 2024

A novel approach to improve driver attention and response during autonomous-to-manual transitions using tactile feedback systems.

PDF DOI

A Unified Framework for Vertical States Estimation: Data-Driven Approach Incorporating Uncertainty

Shangguan, Z., Mrhasli, Y.E., Atheupe, G.P., Mouton, X., Monsuez, B., Tapus, A.

Proceedings of the 2024 4th International Conference on Robotics and Control Engineering, 2024

A framework that improves state estimation in robotic systems by accounting for uncertainty in sensor data.

PDF DOI

Multimodal Practices to Sustain Multiactivity When Live Streaming

Song, L., Shangguan, Z.

Proceedings of the 2024 ACM International Conference on Interactive Media Experiences, 2024

An examination of how live streamers handle multiple tasks simultaneously through multimodal interaction techniques.

PDF DOI

The Moment That The Driver Takes Over: Examining Trust in Full Self-Driving in A Naturalistic and Sequential Approach

Le, S., Zhegong, S.

Proceedings of the 22nd European Conference on Computer-Supported Cooperative Work, 2024

A study on driver trust and handover dynamics in autonomous vehicles using naturalistic observation methods.

PDF DOI

Silicone-Based Haptic Interfaces: Enhancing Multimodal Interactions through Pneumatic Tactile Feedback

Liu, Y., Shangguan, Z., Tapus, A., Safin, S., Détienne, F., Lecolinet, E.

2024 16th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2024

Development of novel silicone-based tactile interfaces for improved human-machine interaction.

PDF DOI

Emotion-Adaptive Pneumatic Haptic Systems for Affective Movie Experiences: Integrating Biomimetic Robotics and Machine Learning

Liu, Y., Shangguan, Z., Safin, S., Détienne, F., Lecolinet, E.

2024 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2024

A system that enhances movie viewing experiences through emotion-responsive haptic feedback.

PDF DOI

Robot Self-Recognition via Facial Expression Sensorimotor Learning

Shangguan, Z., Ding, M., Yu, C., Chen, C., Tapus, A.

2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2023

A method enabling robots to recognize their own facial expressions through sensorimotor learning principles.

PDF DOI

What Do I Look Like? A Conditional GAN Based Robot Facial Self-Awareness Approach

Shangguan, Z., Yu, C., Huang, W., Sun, Z., Tapus, A.

International Conference of Social Robotics (ICSR), 2022

A generative model approach for robots to develop facial self-awareness capabilities.

PDF DOI

Speech-Driven Robot Face Action Generation with Deep Generative Model for Social Robots

Yu, C., Zhang, H., Shangguan, Z., Hei, X., Cangelosi, A., Tapus, A.

International Conference on Social Robotics, 2022

A deep generative model that creates appropriate facial expressions for robots based on speech input.

PDF DOI

What Do I Look Like? Dataset for Social Robot Facial Expression Self-Awareness

Zhegong, S., Yu, C., Tapus, A.

Workshop on Robot Curiosity in Human Robot Interaction (RCHRI), University of Waterloo, 2022

A dataset created to facilitate research in robot self-awareness of facial expressions.

PDF DOI

Driving Behavior Analysis from Multi-Modal Data

Shangguan, Z., Tapus, A.

HiParis Summer School (Poster), 2021

A multi-modal approach to analyzing and understanding driving behaviors.

PDF DOI

Location Information-Assisted Robust Beamforming Design for Ultra-Wideband Communication Systems

Yang, L., Zhang, Z., Fang, X., Cao, S., Shangguan, Z., Li, S.

Symmetry, 2021

A novel approach to enhance beamforming in ultra-wideband communication using location data.

PDF DOI

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Projects

ERC e-TALK

Language is the most natural means of communication among people, as well as for interaction between people and robots. As the great majority of words people use are abstract words, to achieve natural language interaction with robots it is crucial that machines can handle both concrete and abstract concepts. This timely project strategically builds on the recent, substantial advances in developmental psychology and embodied cognition theories on abstract concepts, on developmental robotics and AI methods for cognitive modelling, and in human-robot interaction and language use, to bootstrap our scientific and technological understanding of the grounding of abstract concepts and words in robotic agents.

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Ethics Challenges in Socially Assistive Robots and Agents: Legality, Value Orientation, and Future Design for Human-Robot Interaction (HRI)

We are currently observing the shift of robots transitioning from controlled laboratory environments to more publicly accessible spaces (hospitals, streets, homes, factories, work environments, etc.), engaging in increased interactions with humans. This evolution underscores the growing significance of Human-Robot Interaction (HRI) and brings to the forefront various ethical and legal considerations. The workshop aims to provide participants with a comprehensive understanding of the intricate intersection between technology and ethical considerations in the realm of assistive robotics. Focused on exploring the ethical implications and regulatory frameworks surrounding the integration of AI in assistive robotics, the workshop will focus on the challenges and opportunities inherent in this rapidly advancing field. Participants will gain insights into key ethical principles, legal considerations, and initiatives shaping the responsible development and deployment of assistive robotic technologies. Through discussions, case studies, and expert perspectives, the workshop endeavors to foster a collaborative environment for addressing the evolving ethical landscape and regulatory dynamics in the field of AI assistive robotics.

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ITSC2022 workshop: Human Behavior Understanding (HBU-CAP'22)

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Dr. Zhegong SHANGGUAN 上官哲公

About Me

Research Interests

Selected Publications

Factors Influencing Emotional Driving: Examining the Impact of Arousal on the Interplay Between Age, Personality, and Driving Behaviors

Using a Pneumatic Tactile Steering Wheel to Enhance the Multi-Modal Takeover Request In Smart Vehicle

A Unified Framework for Vertical States Estimation: Data-Driven Approach Incorporating Uncertainty

Multimodal Practices to Sustain Multiactivity When Live Streaming

The Moment That The Driver Takes Over: Examining Trust in Full Self-Driving in A Naturalistic and Sequential Approach

Silicone-Based Haptic Interfaces: Enhancing Multimodal Interactions through Pneumatic Tactile Feedback

Emotion-Adaptive Pneumatic Haptic Systems for Affective Movie Experiences: Integrating Biomimetic Robotics and Machine Learning

Robot Self-Recognition via Facial Expression Sensorimotor Learning

What Do I Look Like? A Conditional GAN Based Robot Facial Self-Awareness Approach

Speech-Driven Robot Face Action Generation with Deep Generative Model for Social Robots

What Do I Look Like? Dataset for Social Robot Facial Expression Self-Awareness

Driving Behavior Analysis from Multi-Modal Data

Location Information-Assisted Robust Beamforming Design for Ultra-Wideband Communication Systems

Projects

ERC e-TALK

Ethics Challenges in Socially Assistive Robots and Agents: Legality, Value Orientation, and Future Design for Human-Robot Interaction (HRI)

ITSC2022 workshop: Human Behavior Understanding (HBU-CAP'22)

Education

Ph.D. in Computer Science

M.Sc. in Computer Technology

B.Sc. in Electronic Science and Technology

News

New Paper Accepted

Presenting at [Conference Name]

New Research Grant

Conference Photos