Exploring Synergy Between Humans and Robots via Interaction Design and Implementation
Project Description
With the rise of Embodied AI, robots are evolving from passive tools into active social agents. However, defining the right relationship between humans and machines is a complex challenge. This project aims to explore the versatile roles of robots in social settings: acting as assistants to support specific tasks, collaborators to facilitate teamwork, or companions to provide social support.

We will utilize platforms ranging from robot cars to humanoid robots (e.g., Grace) to investigate these dynamic roles. Target scenarios include having robots facilitate group work, conduct interviews, and provide support in affective communications and interactions. By integrating Large Language Models (LLMs), we aim to design robots that can intelligently adapt their role to achieve true synergy with humans. Students will gain hands-on experience in robot development and HCI research, with the goal of producing a working demo or a research paper.
Supervisor
MA, Xiaojuan
Quota
10
Course type
UROP1100
UROP2100
UROP3100
UROP4100
Applicant's Roles
- Developing Robot Logic: Designing adaptive algorithms with LLM and other models for robots to complete various tasks as required.
- Implementing Physical Robots: Programming and deploying robotic platforms (e.g., robot cars, humanoid robots like Grace).
- Designing Interactions: Creating approaches for seamless and effective human-robot collaboration.
- Conducting User Studies: Designing, running, and analyzing experiments to evaluate human-robot interactions.
- Prototyping and Research: Assisting in building demos or contributing to a research publication.
Applicant's Learning Objectives
Understand the concepts and frameworks of Human-Robot Interaction (HRI). Learn to design interaction approaches and conduct user-centered studies. Gain hands-on experience in developing intelligent agents and integrating them with physical robots. Acquire skills in programming and implementing adaptive robotic systems.
Complexity of the project
Moderate