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About

Daniel Urieli is a robotics and artificial intelligence researcher whose work centers on autonomous humanoid robot locomotion, motion optimization, and multi-agent systems, with a particular focus on competitive robot soccer. His most significant contributions emerged from his work with the UT Austin Villa team in the RoboCup 3D simulation competition, where he played a central role in designing and optimizing an omnidirectional walk engine for simulated Nao humanoid robots. This work, which earned the team a championship title at RoboCup 2011, demonstrated how physics-based simulation combined with machine learning could produce highly capable, adaptive locomotion strategies. Urieli's research on optimizing interdependent skills addressed a fundamental challenge in robotics: when an agent's capabilities — such as walking speed, turning agility, and kicking power — interact with one another, improving them in isolation can be counterproductive. His framework for joint optimization of these skills offered a principled and practically effective solution. Across his most-cited publications, totaling over 130 citations, Urieli consistently bridged theoretical machine learning with real-world robotic engineering, producing replicable, competition-validated results that have informed subsequent work in humanoid robot control and autonomous agent design.