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Interactive Robotics Laboratory
Yu Gu, Professor

John Little

Little Portrait

Computer Science



    BS Computer Science, Shepherd University (2022)

    MS Computer Science, West Virginia University (EST 2024)


John’s interest in robotics took off with his participation in the swarm robotics focused REU hosted at WVU in the Summer of 2021. He then spent his senior year of his Computer Science program at Shepherd University reestablishing the university's student run robotics lab. Starting with just himself, he gathered a group of interested computer science and engineering students, and through project leadership and peer mentoring helped revitalize the interest in robotics at Shepherd University. He applied for a Master’s program at WVU in Computer Science that same year and after being admitted was offered a Graduate Research Assistant position by Dr. Yu Gu. Coming originally from Maryland, John is a transplant, but having made strong connections during his REU experience he is happy to be working in the IRL lab.


John's research within the realm of swarm robotics aims to uncover the advantages of decentralized systems, emphasizing their potential resilience, scalability, and adaptability. Rooted in both the natural world and economic theories, he explores how mechanisms from ant colonies and principles like free-market economics can be integrated into task allocation algorithms and decision-making protocols for robotic swarms. Utilizing economic principles, reinforcement learning, agent-based modeling, and decision theory as cornerstone methodologies, John seeks to build intelligent swarm systems capable of dynamic adaptations. These systems are not just theoretical constructs; they hold tangible applications in various sectors. While futuristic applications include space exploration and extraplanetary resource gathering, John is equally invested in addressing immediate, real-world challenges such as terrestrial resource collection, logistics optimization, and search and rescue missions.


  • Swarm Robotics
  • Economic Applications
  • Robotic Decision Making
  • Agent-based Modeling
  • Reinforcement Learning