Geordan Gutow

Georgia Institute of Technology, Carnegie Mellon University

Papers

Total Citations

H-Index

About

Geordan Gutow is a robotics researcher whose work lies at the intersection of motion planning, multi-agent coordination, and autonomous construction. His research focuses on developing algorithms that enable robots to operate effectively in complex, uncertain environments—from navigating with guaranteed safety to orchestrating teams of robots building 3D structures. Gutow’s most cited work, "Koopman Operator Method for Chance-Constrained Motion Primitive Planning" (2020, 8 citations), introduces a novel approach to path planning under parametric uncertainty, leveraging Koopman operator theory to ensure probabilistic safety constraints are met. He has since advanced the field of Multi-Agent Collective Construction (MACC), authoring two key papers: "Multi-Agent Collective Construction Using 3D Decomposition" (2023, 4 citations) and "Hierarchical Planning for Long-Horizon Multi-Agent Collective Construction" (2024, 2 citations). These works propose scalable, hierarchical planners that decompose complex building tasks into manageable subproblems, enabling teams of cubic robots to construct large 3D structures block by block. Most recently, Gutow has explored ergodic exploration over meshable surfaces (2025), extending coverage planning to non-Euclidean domains for search and rescue. His contributions are shaping how robots plan and collaborate in the real world.