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About

Joshua Ott is a robotics researcher whose work centers on autonomous exploration, adaptive path planning, and decision-making under uncertainty for mobile robots operating in unknown and extreme environments. His most impactful contribution, "Learning-based methods for adaptive informative path planning" (2024, 35 citations), introduces learning-based techniques that allow robots to dynamically adjust their data-collection strategies, maximizing information gain in initially unknown settings. Ott also developed a method for adaptive coverage path planning (2022, 12 citations) that enables efficient exploration under mission time constraints by maximizing sensor footprint coverage. His work on safe navigation in extreme environments (2023, 8 citations) uses semantic belief graphs to handle perceptual uncertainty, a critical capability for robots navigating terrain with mobility-stressing elements. Ott contributed to the DARPA Subterranean Challenge as part of Team CoSTAR, extending the NeBula autonomy solution to larger-scale environments (2024, 7 citations). He has also advanced active source seeking (2023, 7 citations) with fast, scalable signal inference models, and risk-aware meta-level decision making (2022, 3 citations) for exploration under uncertainty. With over 70 total citations, Ott’s research is shaping the next generation of autonomous robots capable of intelligent, adaptive, and safe exploration in complex, real-world environments.