Robot Socialization Lab
Research on embodied heterogeneous multi-robot systems, where different robotic bodies learn to coordinate without being reduced to the same logic.
Robot Socialization Lab
Embodied Heterogeneous Multi-Robot Systems
Yuan Gao
Shenzhen Institute of Artificial Intelligence and Robotics / The Chinese University of Hong Kong, Shenzhen
Why build labs when most robot systems are designed to work alone, or assume all robots are identical?
The Robot Socialization Lab asks what happens when radically different robotic bodies—humanoids, quadrupeds, drones, wheeled platforms—are required to share one system. They do not share the same sensors. They do not move in the same way. They do not read the environment through the same lens.
Building cooperation across this kind of heterogeneity is not a calibration problem. It requires rethinking coordination from the ground up: how to negotiate, how to trust, how to form temporary alliances, and how to let difference become a resource rather than a liability.
Four ongoing threads that anchor the lab's papers, systems, and public work.
Coordination Across Unlike Bodies
Learning, planning, and emergent strategy for robots with different morphologies, sensing stacks, and action spaces. Focus on negotiation rather than homogenization.
Shared Autonomy
How humans and heterogeneous robot teams can share control, information, and decision-making without one side dominating the other.
Robot Theatre
Turning algorithmic behavior—reinforcement, hesitation, negotiation—into something viewers can physically encounter in public space.
Social Infrastructure
The norms, protocols, and material conditions that allow diverse robot populations to coexist and coordinate over time.
Researchers, artists, and engineers working across embodied intelligence and heterogeneous systems.
Yuan Gao
Principal InvestigatorResearcher and artist working across robotics, machine learning, and public installation. Ph.D. from Uppsala University.
[Student Name]
Ph.D. StudentResearch focus and brief background.
[Student Name]
Ph.D. StudentResearch focus and brief background.
[Student Name]
Research Assistant / Visiting ScholarResearch focus and brief background.
For collaboration, visiting, or research inquiries, please reach out via the contact page.