We are developing robotics game platform where strategy meets embodiment. Blending game theory, autonomous systems, and real-world hardware, this platform challenges teams to design intelligent agents that compete, cooperate, and adapt under uncertainty. Unlike traditional robotics contests focused on performance, it emphasizes decision-making, adversarial reasoning, and motion-based signaling—turning game play into a experimental platform for multi-agent systems research. Designed for both education and research, it provides a scalable ecosystem where theory is tested in action.

Student: Alexia De Costa

This project develops a companion robot designed to support research in human–robot interaction and privacy-aware design. It features camera-based facial detection, expressive gaze behaviors, audio responses, and various soft and rigid materials to mimic a household cat. Because camera systems can enhance interaction while raising privacy concerns, the ongoing study compares peoples’ responses under two conditions: a clear, high-quality camera filter and a blurred, low-clarity camera filter. Using surveys and observation of touch behavior, the study examines how camera clarity shapes engagement and perceived privacy, informing the design of social robots that are effective while respecting user comfort.

We participate in an autonomous blimp competition where lighter-than-air (LTA) robots are tasked with "capturing" the game balls and "shooting" them through the goals sustained from the ceiling. More than five universities participate in this competition, and the most recent one was hosted in the Eagle Bank Arena at George Mason University.  

News capture by Fox 5

Through this competition we showcase the product of our research listed below.

As part of the effort towards DTR blimp competition, we are developing flapping-wing blimps. One of our first prototype is called "Flappy". 

More information about flappy can be found here.

Students: Kentaro Nojima-schmunk, James Yang, David Turzak, Kevin Kim, Andrew Vu, Sree Motukuri

Collaborator: Ningshi Yao (GMU)

This project aims to build robotic agents that can robustly operate in dynamic and contested environments using sensor-actuator pairs that are distributed in the system. We address two weaknesses that conventional robot design approach has: the centralized architecture reliant on CPU that causes brittleness, and the top-down approach based on idealized mathematical models that forces us to pursue precision. We explore the viability of a bottom-up approach in building intelligence into a robot or a group of robots using individually weak components that interact with each other. We will specifically study how those components can utilize analog interaction for achieving faster response time and better tolerance to imprecision. Through the case-studies using Lighter-Than-Air (LTA) vehicles, we seek to characterize the robustness of such systems and construct a generalizable theory for their analysis.