Amanda Bouman
Papers
9
Total Citations
282
H-Index
7
About
Amanda Bouman is a leading roboticist whose research focuses on autonomous exploration, multi-robot coordination, and decision-making under uncertainty in extreme, communication-constrained environments. As a key member of TEAM CoSTAR, she made foundational contributions to the NeBula autonomy solution, which won Phase II of the DARPA Subterranean Challenge. Her work on NeBula (105 citations) and its extensions (51 citations) integrates novel algorithms, hardware, and software for robust subterranean navigation. Bouman pioneered hierarchical value learning with PLGRIM (46 citations), enabling efficient large-scale exploration by accounting for sensor and localization uncertainty. She also developed ACHORD (37 citations), a communication-aware coordination framework that allows multi-robot teams to maintain intermittent connectivity while maximizing coverage. Notably, Bouman led early efforts to deploy Boston Dynamics’ Spot robot for long-range autonomous exploration (14 citations), pushing the boundaries of legged locomotion in extreme environments. Her adaptive coverage path planning (12 citations) and risk-aware meta-level decision-making (3 citations) further advance autonomous systems’ ability to explore unknown, hazardous spaces. With over 280 total citations, Bouman’s work directly enables resilient, scalable robotic autonomy for real-world subterranean and planetary exploration.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9Risk-aware Meta-level Decision Making for Exploration Under Uncertainty3 citations · 2022