Max Robotics — Global Service Robot Directory + US Certification

About

Or Tslil is a versatile researcher whose work spans robotics, distributed estimation, and autonomous systems. Best known for their 2016 study on robotic swing-up regrasping manipulation — the most cited work in their portfolio with 30 citations — Tslil developed an innovative framework combining the impulse-momentum approach with constrained Linear Quadratic Regulator (cLQR) control to enable robotic arms to reorient grasped objects with precision and efficiency, a contribution with significant implications for dexterous manipulation in industrial and service robotics. Beyond robotics, Tslil has made meaningful contributions to the field of distributed sensor fusion. Their 2019 paper on Log-linear Chernoff Fusion for distributed particle filtering addresses a critical challenge in large-scale sensor networks: maintaining statistical consistency when cross-correlations between nodes are unknown. This work, along with their 2020 follow-up on Chernoff fusion approaches, demonstrates a rigorous theoretical engagement with robust estimation under uncertainty. Tslil has also explored semantic Simultaneous Localization and Mapping (SLAM), proposing probabilistic representations for object identities that enrich environmental understanding for autonomous agents. Collectively, their research reflects a rare ability to bridge control theory, probabilistic inference, and autonomous robotics — making their work valuable reading for researchers working at these intersections.