Biography
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
Compact Symbolic Representation Learning for Efficient Planning from Unsupervised Interaction
Compact Symbolic Representation Learning for Efficient Planning from Unsupervised Interaction
Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards
Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards
No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation
No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation
XQCfD: Accelerating Fast Actor-Critic Algorithms with Prior Data and Prior Policies
XQCfD: Accelerating Fast Actor-Critic Algorithms with Prior Data and Prior Policies
Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces
Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces
