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He is Professor of Computer Science at Stanford University.
His research focuses on methodologies and technologies in human-centered robotics including humanoid control architectures, human motion synthesis, interactive dynamic simulation, haptics, and human-friendly robot design. He is Co-Editor of the Springer Tracts in Advanced Robotics (STAR) series and the Springer Handbook of Robotics, which received the PROSE Award for Excellence in Physical Sciences & Mathematics.
Ocean One’s journey in the Mediterranean marks a new level of marine exploration: Much as past technological innovations have impacted society, Ocean One’s ability to distance humans physically from dangerous and unreachable work spaces while connecting their skills, intuition, and experience to the task promises to fundamentally alter remote work.
He currently serves on the Administrative Committee of the IEEE Robotics and Automation Society, and the editorial board of the Autonomous Robots journal, and is the Program Chair for IROS 2017.
Abstract: The promise of oceanic discovery has intrigued scientists and explorers for centuries, whether to study underwater ecology and climate change, or to uncover natural resources and historic secrets buried deep at archaeological sites.
Traditionally machine learning has focused on producing a single best prediction.
We build an efficient framework for making multiple predictions where the objective is to optimise any utility function which is (monotone) submodular over a sequence of predictions.