Papers
30
Total Citations
737
H-Index
11
About
Edward Schmerling is a robotics and autonomous systems researcher whose work spans motion planning, safety assurance, and the integration of machine learning into real-time control. His early contributions focused on sampling-based motion planning algorithms, where he developed convex optimization techniques to generate smooth, dynamically feasible trajectories for car-like robots — work that has accumulated over 100 citations and addressed a longstanding limitation of jagged planner outputs. Alongside this, his research on optimal sampling-based planning under differential constraints helped establish theoretical guarantees in a previously open problem space. Schmerling has made significant strides in uncertainty-aware planning, with his Monte Carlo motion planning framework (105 citations) and reachability-based safety assurance methods for human-robot vehicle interactions (93 citations) demonstrating a sustained commitment to robust, real-world autonomous driving. More recently, he has pioneered the application of large language models to robotic anomaly detection and reactive planning, exploring how foundation models can enable zero-shot generalization to out-of-distribution failure scenarios. His work on conformal prediction for safety assurances and data-driven spectral submanifold reduction for high-dimensional control further reflects a researcher pushing the boundaries of both principled safety and scalable autonomy.
Research Focus
Key Achievements
Top Papers
- 1
- 2Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty105 citations · 2017
- 3
- 4Semantic anomaly detection with large language models85 citations · 2023
- 5
- 6CoCo: Online Mixed-Integer Control Via Supervised Learning36 citations · 2021
- 7Real-Time Anomaly Detection and Reactive Planning with Large Language Models28 citations · 2024
- 8
- 9Sample-Efficient Safety Assurances Using Conformal Prediction25 citations · 2022
- 10