Greetings,

I am Lixiao (Sean) Zhu 朱礼箫

Human-Robot Interaction Researcher and Roboticist

I am a researcher in human-robot interaction (HRI), with a particular interest in how robots can be designed and deployed to interact with people in safe, effective, and meaningful ways. My research explores human responses to robotic behavior and the factors that shape collaboration and interaction between humans and robots.

With a strong background in programming and a broad interest in emerging technologies, I enjoy working at the intersection of robotics, human behavior, and responsible technology. Looking ahead to my expected graduation in 2027, I am interested in opportunities where I can contribute to research and development in robotics, robot safety, and robot ethics, particularly in applications involving human-robot interaction.

Outside of research, I am an avid gamer and enjoy exploring technology through hands-on projects. More recently, I have developed an interest in mechanical watches and watch building, which has given me another way to explore precision engineering, mechanical systems, and craftsmanship.

I am always interested in connecting with researchers, engineers, and organizations working on the future of robotics, particularly those interested in HRI, robot safety, and responsible robotics.

Pushing the Boundaries of Human-Robot Interaction

Raise VR Demo.mp4

Assessing Nonverbal Synchronization in Human-Robot Interaction via VR and AR

This work investigates how people adapt their movements to changes in robot speed, and whether these responses differ between physical robot (PR) and virtual reality (VR) environments.

Initial trials revealed a significant interaction between robot speed and robot form, with participants showing greater sensitivity to speed changes when interacting with the physical robot. We explore several possible explanations, including richer multimodal sensory feedback, greater perceived safety and collision risk, and differences in cognitive load associated with VR.

The study includes 45 participants, with an additional 6 participants recruited to examine potential ordering effects between the physical and virtual conditions. By comparing these environments, this work aims to better understand how physical presence and sensory context influence human adaptation and synchronization with robots.

Zhu-TeaserVideo.mp4

Zhu-Poster.pdf

Investigating Robot Influence on Human Behaviour By Leveraging Entrainment Effects

Humans naturally tend to synchronize their movements with others, a phenomenon known as the entrainment effect, whether voluntarily or involuntarily. This phenomenon extends to interactions between humans and robots, which could have either positive or negative consequences for the human partner. We propose a human-subject study aimed at investigating the use of robots to influence human behaviour through entrainment in diverse Human-Robot Interaction (HRI) scenarios. The current work involves two human-subject experiments investigating the impact of robots on short-term human behaviour, encompassing human-human and human-robot interactions. The goal is to comprehend how variations in robot actions, such as movement frequency during repetitive tasks, influence human perceptions and behaviours in collaborative lab-based settings. Another objective is to investigate the factors that make participants aware of the entrainment effect during HRI. The preliminary results of the HHI experiment provide evidence that individuals tend to synchronize their movements with another person.

3610978.3638367.pdf

HRI-speedup2.mp4

Effects of Proactive Explanations by Robots on Human-Robot Trust

zhu2020icsr.Mp4

The performance of human-robot teams depends on human-robot trust, which in turn depends on appropriate robot-to-human transparency. A key way for robots to build trust through transparency is by providing appropriate explanations for their actions. While most previous work on robot explanation generation has focused on robots’ ability to provide post-hoc explanations upon request, in this paper we instead examine proactive explanations generated before actions are taken, and the effect this has on human-robot trust. Our results suggest a positive relationship between proactive explanations and human-robot trust, and reveal fundamental new questions into the effects of proactive explanations on the nature of humans’ mental models and the fundamental nature of human-robot trust.

ICSR_poster_Lixiao_Zhu.pdf

ICSR_Proactive_Explanation.pdf

Get in touch at lixiao.zhu[at]outlook.com

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