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ARIA Opportunity Space: Smarter Robot Bodies
AI for Robot World Models
Today’s robots cannot reliably grasp and manipulate objects while moving dynamically, limiting their usefulness in complex, unpredictable environments. Encode fellow Edward Grant is working with Dimitrios Kanoulas and Lorenzo Jamone at University College London to develop a predictive world-model controller that enables agile, coordinated loco-manipulation by learning compact, uncertainty-aware latent dynamics. This research could transform robotics in emergency response, logistics, agriculture, and space, enabling robots to act fluidly and intelligently in real-world missions where every second counts.
Dimitrios Kanoulas' Lab
University College London
,
London
Kanoulas Lab
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Ed holds a PhD in Computer Science from UCL and was previously co-founder and CSO of Rahko (acquired by Odyssey Therapeutics), a quantum machine learning company. He subsequently became Head of Machine Learning at Odyssey Therapeutics.
Dimitrios Kanoulas
Professor of Robotics and AI; UKRI Future Leaders Fellow
Dimitrios Kanoulas is Professor of Robotics at University College London (UCL), where he leads research on robot perception, cognition, and learning for intelligent machines operating in unstructured and uncertain environments. His work focuses on enabling highly articulated robots, such as legged robots, to perceive, navigate, and interact safely and effectively in complex natural terrains. Robotics has the potential to improve safety, health, and comfort in society, and his research advances this vision by developing cognitive systems that allow robots to sense, adapt, and make decisions in dynamic real-world settings.
Lorenzo Jamone
Associate Professor in Robotics & AI at UCL Department of Computer Science
Lorenzo Jamone is Associate Professor in Robotics and AI at the Department of Computer Science of University College London (UCL), where he leads the CRISP group: Cognitive Robotics and Intelligent Systems for the People. The main focus of the group is on researching the "intelligence of the hand", aiming to create dexterous robotic systems that can use their hands as smartly as humans do, but also to better understand the cognitive processes behind human dexterity through the use of robotic technologies and computational models. This research has generated over 140 publications (H-index 31) in the areas of cognitive robotics, robot learning, robotic manipulation, and tactile sensing.
