Papers
12
Total Citations
113
H-Index
6
About
Matthew C. Fontaine is a researcher at the intersection of robotics, multi-agent systems, and evolutionary computation, with a particular focus on human-robot interaction and automated warehousing. His work is distinguished by a creative application of Quality Diversity (QD) algorithms — most notably MAP-Elites and its variants — to generate diverse, challenging scenarios that stress-test robot coordination systems in ways traditional optimization methods cannot. Fontaine's most cited contributions explore how environments, not just robot policies, fundamentally shape coordination behavior in human-robot teams, earning 27 and 17 citations respectively for uncovering this underexplored dynamic. He has further advanced the field by demonstrating that QD-driven scenario generation can rigorously evaluate shared autonomy algorithms, offering researchers a scalable computational framework for benchmarking. In automated warehousing, his work optimizing both layout design and multi-agent path finding has attracted nearly 30 combined citations, highlighting practical industrial impact. Beyond robotics, Fontaine has applied QD methods to game design, exploring deck spaces in Hearthstone as an early demonstration of their broader applicability. His development of scalable neural cellular automata for environment generation and refined covariance matrix adaptation techniques further cements his reputation as an innovative contributor to the growing QD research community.
Research Focus
Key Achievements
Top Papers
- 1On the Importance of Environments in Human-Robot Coordination27 citations · 2021
- 2Multi-Robot Coordination and Layout Design for Automated Warehousing19 citations · 2023
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- 8On the Importance of Environments in Human-Robot Coordination5 citations · 2021
- 9Arbitrarily Scalable Environment Generators via Neural Cellular Automata5 citations · 2023
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