Oriana Peltzer
Stanford University, Vaughn College of Aeronautics and Technology
Papers
5
Total Citations
31
H-Index
4
About
Oriana Peltzer is a leading roboticist whose research sits at the intersection of autonomous navigation, decision-making under uncertainty, and multi-agent coordination. Her work focuses on enabling robots to operate safely and efficiently in the most challenging environments—from extreme subterranean terrains to cluttered, signal-dense spaces. Peltzer’s major contributions include the development of semantic belief graphs for terrain-aware planning, which allows robots to adapt their locomotion to mobility-stressing elements, and her extensions to the NeBula autonomy solution, which scaled Team CoSTAR’s DARPA Subterranean Challenge system to larger, more complex environments. She has also pioneered algorithms for active source seeking, using fast signal inference to locate sources in unknown settings, and introduced STT-CBS, a conflict-based search method for multi-agent pathfinding with stochastic travel times. Her risk-aware meta-level decision-making framework further advances exploration under uncertainty. With her most-cited works accumulating over 30 citations since 2020, Peltzer’s research is pivotal for the next generation of autonomous systems, promising safer and more intelligent robots for extreme and unstructured domains.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Fast and Scalable Signal Inference for Active Robotic Source Seeking7 citations · 2023
- 4
- 5Risk-aware Meta-level Decision Making for Exploration Under Uncertainty3 citations · 2022