
INSPECTOR
Autonomous culvert inspection with legged robots in challenging field conditions.

We broadly work in algorithms for autonomy with an emphasis on perception, representation and reasoning in robotics. Most of our research is motivated by real-world problems from our field deployments. Several of our projects are in collaboration with experts in related fields as well as domain scientists. Our lab has received support from federal agencies such as NSF, DARPA, ONR, AFOSR, AFRL, REMADE. We are also thankful for support from MOOG Inc.

Autonomous culvert inspection with legged robots in challenging field conditions.

Perception, planning, and hydraulic control for autonomous excavation.

Allocate limited flight time to informative measurements of vegetation condition.

Connect terrain representations and mobility models to robust mission planning.

Learn how to control onboard lighting for more reliable robot perception.

Adapt illumination and camera exposure for visual estimation in dark environments.

Learn which pixels to trust for more reliable, efficient visual navigation.

Evaluate 3D reconstructions through resolution, accuracy, coverage, and artifacts.

Constraint-aware trajectory sampling for safe, efficient robot motion.

Connect robot–terrain interaction with locomotion adaptation and risk-aware navigation.

Language-guided observation and navigation for autonomous culvert inspection.
Around the lab
September 26, 2026
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August 2026
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