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About

Sadegh Rabiee is a leading researcher in autonomous mobile robotics, with a focus on high-speed off-road navigation, kinodynamic modeling, and robust perception. His work bridges the gap between classical robotics and modern learning-based approaches to enable reliable robot operation in challenging, unstructured environments. Rabiee's most influential contributions center on inverse kinodynamics (IKD) for high-speed robot control. His paper "VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics" (31 citations) addresses the critical challenge of vehicle-terrain interaction variability. He further advanced this area with "High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization" (17 citations), demonstrating how learned models can capture complex kinodynamic effects for precise control. His earlier work on "A Friction-Based Kinematic Model for Skid-Steer Wheeled Mobile Robots" (40 citations) provided foundational insights into slippage compensation. Beyond motion control, Rabiee has made notable contributions to perception and fleet management. His "Robofleet" system (24 citations) enables reliable communication for multi-robot deployments, while his introspective perception framework (IV-SLAM, IVOA) addresses the critical problem of uncertainty estimation in vision-based sensing. His work on "Visual Representation Learning for Preference-Aware Path Planning" (17 citations) extends robot autonomy to consider terrain preferences for safety and deployer requirements. With over 180 total citations, Rabiee's research continues to push the boundaries of what autonomous robots can achieve in real-world, off-road environments.