Sarah Koehler

Papers

Total Citations

H-Index

About

Sarah Koehler is a roboticist whose research bridges the gap between high-level task planning and low-level physical control, with a particular focus on mobile and modular robotic systems. Her most influential work, "Fully Proprioceptive Slip-Velocity-Aware State Estimation for Mobile Robots via Invariant Kalman Filtering and Disturbance Observer" (2023, 17 citations), introduces a novel state estimator that combines inertial measurement unit data with body velocity measurements using a Right Invariant Extended Kalman Filter and a Disturbance Observer. This fully proprioceptive approach enables robots to accurately estimate their state even during wheel slip—a critical capability for robust navigation in unstructured environments. Koehler’s earlier foundational work, "High-level control of modular robots" (2011, 12 citations), addresses the challenge of translating high-level tasks expressed in structured English into provably correct control for modular robots, incorporating constraints on geometry and motion. This work demonstrates her ability to tackle complex, multi-layered problems in robotics, from formal verification to practical implementation. With a career spanning over a decade, Koehler’s contributions are shaping how robots perceive their own motion and how they can be programmed intuitively for diverse tasks.