Ingmar leads the Applied Artificial Intelligence Lab (A2I) at Oxford University. His goal is to enable robots to robustly and effectively operate in complex, real-world environments. His research is guided by a vision to create machines which constantly improve through experience. In doing so Ingmar’s work explores a number of intellectual challenges at the heart of robot learning, such as unsupervised scene interpretation and action inference, machine introspection in perception and decision making, data efficient learning from demonstration, transfer learning and the learning of complex tasks via a curriculum of less complex ones. All the while Ingmar’s research remains grounded in real-world robotics applications such as manipulation, autonomous driving, logistics and space exploration. Ingmar is recipient of a number of best paper awards at leading international venues in robotics research and AI. He is a founding Director of the Oxford Robotics Institute, which has forged an international reputation for excellence in robotics research. In 2014 Ingmar co-founded Oxbotica, a leading provider of mobile autonomy software solutions.
Teaching Material: C18 Mobile Robotics & Navigation Code Pack: available here.
Most Recent Publications
First steps: latent-space control with semantic constraints for quadruped locomotion
Mitchell A, Engelcke M, Parker Jones O, Surovik D, Havoutis I et al. (2021), Proceedings of the IEEE International Workshop on Intelligent Robots and Systems (IROS), 5343-5350
Fuchs FB, Wagstaff E, Dauparas J & Posner I (2021), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12829 LNCS, 585-595
RELATE: physically plausible multi-object scene synthesis using structured latent spaces
Ehrhardt S, Groth O, Monszpart A, Engelcke M, Posner H et al. (2020), NIPS Proceedings, 33
Attention-privileged reinforcement learning
Salter S, Rao D, Wulfmeier M, Hadsell R & Posner H (2020), Proceedings of the Conference on Robot Learning 2020(2020)
Under the radar: learning to predict robust keypoints for odometry estimation and metric localisation in radar
Barnes D & Posner H (2020), Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), 9484-9490