Biography
Driven by an early passion for AI and machine learning in robotics, Ingmar read Electronic Systems Engineering at Aston University before joining Oxford's Department of Engineering Science.
Ingmar's work is frequently covered in the national and international press. In 2014 Ingmar co-founded Oxbotica, a leading provider of mobile autonomy software solutions.
Research Interests
Ingmar leads the Applied Artificial Intelligence Lab (A2I) at Oxford University. He also serves as Deputy Director of the Oxford Robotics Institute, which he co-founded in 2016. Ingmar has a significant track record in designing machine learning approaches (shallow and deep) which address core challenges in AI and machine learning.
His goal is to enable robots to robustly and effectively operate in complex, realworld 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 machine introspection in perception and decision making, data efficient learning from demonstration, transfer learning and the learning of complex tasks via a set of less complex ones.
All the while, Ingmar’s intellectual curiosity remains grounded in real-world robotics applications such as autonomous driving, logistics, manipulation and space exploration. In 2014 Ingmar co-founded Oxbotica, a leading provider of mobile autonomy software solutions.
Research Groups
Recent Publications
Offline Adaptation of Quadruped Locomotion using Diffusion Models
O'Mahoney R, Mitchell AL, Yu W, Posner I & Havoutis I (2024)
BibTeX
@misc{offlineadaptati-2024/11,
title={Offline Adaptation of Quadruped Locomotion using Diffusion Models},
author={O'Mahoney R, Mitchell AL, Yu W, Posner I & Havoutis I},
year = "2024"
}
A Review of Differentiable Simulators
Newbury R, Collins J, He K, Pan J, Posner I et al. (2024), IEEE Access, PP(99), 1-1
BibTeX
@article{areviewofdiffer-2024/7,
title={A Review of Differentiable Simulators},
author={Newbury R, Collins J, He K, Pan J, Posner I et al.},
journal={IEEE Access},
volume={PP},
pages={1-1},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
year = "2024"
}
Oscillating latent dynamics in robot systems during walking and reaching
Parker Jones O, Mitchell A, Yamada J, Merkt W, Geisert M et al. (2024), Scientific Reports, 14(1)
BibTeX
@article{oscillatinglate-2024/5,
title={Oscillating latent dynamics in robot systems during walking and reaching},
author={Parker Jones O, Mitchell A, Yamada J, Merkt W, Geisert M et al.},
journal={Scientific Reports},
volume={14},
number={11434},
publisher={Springer Nature},
year = "2024"
}
Gaitor: Learning a Unified Representation Across Gaits for Real-World Quadruped Locomotion
Mitchell AL, Merkt W, Papatheodorou A, Havoutis I & Posner I (2024)
BibTeX
@misc{gaitorlearninga-2024/5,
title={Gaitor: Learning a Unified Representation Across Gaits for Real-World Quadruped Locomotion},
author={Mitchell AL, Merkt W, Papatheodorou A, Havoutis I & Posner I},
year = "2024"
}
TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfer
Yamada J, Rigter M, Collins J & Posner I (2024), 00, 9190-9196
BibTeX
@inproceedings{twistteacherstu-2024/5,
title={TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfer},
author={Yamada J, Rigter M, Collins J & Posner I},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
pages={9190-9196},
year = "2024"
}
Neural latent geometry search: product manifold inference via Gromov-Hausdorff-informed Bayesian optimization
S??ez de Oc??riz Borde H, Arroyo ??, Morales L??pez I, Posner I & Dong X (2024), Advances in Neural Information Processing Systems 36 (NeurIPS 2023)
BibTeX
@inproceedings{neurallatentgeo-2024/2,
title={Neural latent geometry search: product manifold inference via Gromov-Hausdorff-informed Bayesian optimization
},
author={S??ez de Oc??riz Borde H, Arroyo ??, Morales L??pez I, Posner I & Dong X},
booktitle={37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023)},
year = "2024"
}
DreamUp3D: Object-Centric Generative Models for Single-View 3D Scene Understanding and Real-to-Sim Transfer
Wu Y, Saez de Ocariz Borde H, Collins J, Jones OP & Posner I (2024), IEEE Robotics and Automation Letters
BibTeX
@article{dreamupdobjectc-2024/1,
title={DreamUp3D: Object-Centric Generative Models for Single-View 3D Scene Understanding and Real-to-Sim Transfer},
author={Wu Y, Saez de Ocariz Borde H, Collins J, Jones OP & Posner I},
journal={IEEE Robotics and Automation Letters},
year = "2024"
}
Efficient Skill Acquisition for Insertion Tasks in Obstructed Environments
Yamada J, Collins J & Posner I (2024), Proceedings of Machine Learning Research, 242, 615-627
BibTeX
@inproceedings{efficientskilla-2024/1,
title={Efficient Skill Acquisition for Insertion Tasks in Obstructed Environments},
author={Yamada J, Collins J & Posner I},
pages={615-627},
year = "2024"
}
REWARD-FREE CURRICULA FOR TRAINING ROBUST WORLD MODELS
Rigter M, Jiang M & Posner I (2024), 12th International Conference on Learning Representations, ICLR 2024
BibTeX
@inproceedings{rewardfreecurri-2024/1,
title={REWARD-FREE CURRICULA FOR TRAINING ROBUST WORLD MODELS},
author={Rigter M, Jiang M & Posner I},
year = "2024"
}
AutoGraph: predicting lane graphs from traffic observations
Zurn J, Posner I & Burgard W (2023), IEEE Robotics and Automation Letters, 9(1), 73-80
BibTeX
@article{autographpredic-2023/11,
title={AutoGraph: predicting lane graphs from traffic observations},
author={Zurn J, Posner I & Burgard W},
journal={IEEE Robotics and Automation Letters},
volume={9},
pages={73-80},
publisher={IEEE},
year = "2023"
}
