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
Next steps: learning a disentangled gait representation for versatile quadruped locomotion
Mitchell A, Merkt W, Geisert M, Gangapurwala S, Engelcke M et al. (2022), 2022 International Conference on Robotics and Automation (ICRA), 10564-10570
BibTeX
@inproceedings{nextstepslearni-2022/7,
title={Next steps: learning a disentangled gait representation for versatile quadruped locomotion},
author={Mitchell A, Merkt W, Geisert M, Gangapurwala S, Engelcke M et al.},
booktitle={International Conference on Robotics and Automation (ICRA 2022)},
pages={10564-10570},
year = "2022"
}
Semantically grounded object matching for robust robotic scene rearrangement
Goodwin W, Vaze S, Havoutis I & Posner H (2022), Proceedings of the International Conference on Robotics and Automation (ICRA 2022), 11138-11144
BibTeX
@inproceedings{semanticallygro-2022/7,
title={Semantically grounded object matching for robust robotic scene rearrangement},
author={Goodwin W, Vaze S, Havoutis I & Posner H},
booktitle={International Conference on Robotics and Automation (ICRA 2022)},
pages={11138-11144},
year = "2022"
}
Fast-MbyM: leveraging translational invariance of the fourier transform for efficient and accurate radar odometry
Weston R, Gadd M, De Martini D, Newman P & Posner H (2022), Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2022), 2186-2192
BibTeX
@inproceedings{fastmbymleverag-2022/7,
title={Fast-MbyM: leveraging translational invariance of the fourier transform for efficient and accurate radar odometry},
author={Weston R, Gadd M, De Martini D, Newman P & Posner H},
booktitle={IEEE International Conference on Robotics and Automation (ICRA 2022)},
pages={2186-2192},
year = "2022"
}
Reaching through latent space: From joint statistics to path planning in manipulation
Hung C-M, Zhong S, Goodwin W, Parker Jones O, Engelcke M et al. (2022), IEEE Robotics and Automation Letters
BibTeX
@article{reachingthrough-2022/2,
title={Reaching through latent space: From joint statistics to path planning in manipulation},
author={Hung C-M, Zhong S, Goodwin W, Parker Jones O, Engelcke M et al.},
journal={IEEE Robotics and Automation Letters},
publisher={Institute of Electrical and Electronics Engineers},
year = "2022"
}
Universal Approximation of Functions on Sets
Wagstaff E, Fuchs FB, Engelcke M, Osborne MA & Posner I (2022), Journal of Machine Learning Research, 23
BibTeX
@article{universalapprox-2022/1,
title={Universal Approximation of Functions on Sets},
author={Wagstaff E, Fuchs FB, Engelcke M, Osborne MA & Posner I},
journal={Journal of Machine Learning Research},
volume={23},
year = "2022"
}
Zero-Shot Category-Level Object Pose Estimation
Goodwin W, Vaze S, Havoutis I & Posner I (2022), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13699 LNCS, 516-532
BibTeX
@inproceedings{zeroshotcategor-2022/1,
title={Zero-Shot Category-Level Object Pose Estimation},
author={Goodwin W, Vaze S, Havoutis I & Posner I},
pages={516-532},
year = "2022"
}
GENESIS-V2: inferring unordered object representations without iterative refinement
Engelcke M, Parker Jones O & Posner I (2021), Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
BibTeX
@inproceedings{genesisvinferri-2021/12,
title={GENESIS-V2: inferring unordered object representations without iterative refinement},
author={Engelcke M, Parker Jones O & Posner I},
booktitle={ 35th Conference on Neural Information Processing Systems (NeurIPS 2021)},
year = "2021"
}
There and back again: learning to simulate radar data for real-world applications
Weston R, Jones OP & Posner H (2021), Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), 12809-12816
BibTeX
@inproceedings{thereandbackaga-2021/10,
title={There and back again: learning to simulate radar data for real-world applications},
author={Weston R, Jones OP & Posner H},
booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
pages={12809-12816},
year = "2021"
}
Goal-conditioned end-to-end visuomotor control for versatile skill primitives
Groth O, Hung C, Vedaldi A & Posner I (2021), 2021 IEEE International Conference on Robotics and Automation (ICRA), 1319-1325
BibTeX
@inproceedings{goalconditioned-2021/10,
title={Goal-conditioned end-to-end visuomotor control for versatile skill primitives},
author={Groth O, Hung C, Vedaldi A & Posner I},
booktitle={2021 International Conference on Robotics and Automation (ICRA 2021)},
pages={1319-1325},
year = "2021"
}
Introspective visuomotor control: exploiting uncertainty in deep visuomotor control for failure recovery
Hung C-M, Sun L, Wu Y, Havoutis I & Posner I (2021), Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), 6293-6299
BibTeX
@inproceedings{introspectivevi-2021/10,
title={Introspective visuomotor control: exploiting uncertainty in deep visuomotor control for failure recovery},
author={Hung C-M, Sun L, Wu Y, Havoutis I & Posner I},
booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
pages={6293-6299},
year = "2021"
}
