Biography
Paul Newman is the BP Professor of Information Engineering at the University of Oxford. He is a member of the Oxford Robotics Institute within the Department of Engineering Science. The ORI enjoys a world leading reputation in mobile autonomy, developing machines which roll, walk, poke, swim and fly in the real world.
His focus lies on pushing the boundaries of navigation and autonomy techniques in terms of both endurance and scale. In 2014 he founded Oxbotica, a spinout company focused on Mobile Autonomy. He was elected fellow of the Royal Academy of Engineering and the IEEE with a citation for outstanding contributions to robot navigation.
Research Interests
- Mobile Robotics
- Computer Vision
- ML
- Navigation
- Systems
- Autonomous Vehicles
Research Groups
Recent Publications
Fantastic features and where to find them: a probing method to combine features from multiple foundation models
Ramtoula B, Lajoie P-Y, Newman P & De Martini D (2026), Advances in Neural Information Processing Systems 38: 39th Conference on Neural Information Processing Systems (NeurIPS 2025), 99925-99954
BibTeX
@inproceedings{fantasticfeatur-2026/8,
title={Fantastic features and where to find them: a probing method to combine features from multiple foundation models},
author={Ramtoula B, Lajoie P-Y, Newman P & De Martini D},
booktitle={38th Advances in Neural Information Processing Systems (NeurIPS 2025)},
pages={99925-99954},
year = "2026"
}
Multi-Robot Planning and Control from CCTV Camera Networks in a Real Warehouse
Robinson L, Ramtoula B, Izaaryene A, Newman P & De Martini D (2026)
BibTeX
@misc{multirobotplann-2026/6,
title={Multi-Robot Planning and Control from CCTV Camera Networks in a Real Warehouse},
author={Robinson L, Ramtoula B, Izaaryene A, Newman P & De Martini D},
year = "2026"
}
Robot-relay: building-wide, calibration-less visual servoing with learned sensor handover networks
Robinson L, Gadd M, Newman P & Martini DD (2025), Autonomous Robots, 50(1)
BibTeX
@inproceedings{robotrelaybuild-2025/11,
title={Robot-relay: building-wide, calibration-less visual servoing with learned sensor handover networks},
author={Robinson L, Gadd M, Newman P & Martini DD},
year = "2025"
}
Bayesian Radar Cosplace: Directly estimating location uncertainty in radar place recognition
Agarwal S, Yuan J, Newman P, De Martini D & Gadd M (2025), IET Radar Sonar & Navigation, 19(1)
BibTeX
@article{bayesianradarco-2025/3,
title={Bayesian Radar Cosplace: Directly estimating location uncertainty in radar place recognition},
author={Agarwal S, Yuan J, Newman P, De Martini D & Gadd M},
journal={IET Radar Sonar & Navigation},
volume={19},
publisher={Institution of Engineering and Technology (IET)},
year = "2025"
}
Radar Doppler Dataset
Rennie F, Williams D, Newman P & De Martini D (2025)
BibTeX
@misc{radardopplerdat-2025/1,
title={Radar Doppler Dataset},
author={Rennie F, Williams D, Newman P & De Martini D},
year = "2025"
}
NeuralFloors++: consistent street-level scene generation from BEV semantic maps
Musat V, De Martini D, Gadd M & Newman P (2024), 12872-12879
BibTeX
@inproceedings{neuralfloorscon-2024/12,
title={NeuralFloors++: consistent street-level scene generation from BEV semantic maps},
author={Musat V, De Martini D, Gadd M & Newman P},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)},
pages={12872-12879},
year = "2024"
}
NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms
Panagiotaki E, De Martini D, Kunze L & Veličković P (2024)
BibTeX
@misc{naricpneuralexe-2024/10,
title={NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms},
author={Panagiotaki E, De Martini D, Kunze L & Veličković P},
year = "2024"
}
What you see is what you get: experience ranking with deep neural dataset-to-dataset similarity for topological localisation
Gadd M, Ramtoula B, De Martini D & Newman P (2024), Experimental Robotics: The 18th International Symposium, 595-607
BibTeX
@inproceedings{whatyouseeiswha-2024/8,
title={What you see is what you get: experience ranking with deep neural dataset-to-dataset similarity for topological localisation},
author={Gadd M, Ramtoula B, De Martini D & Newman P},
booktitle={18th International Symposium on Experimental Robotics (ISER 2023)},
pages={595-607},
year = "2024"
}
Masked γ-SSL: learning uncertainty estimation via masked image modeling
Williams DSW, Gadd M, Newman P & De Martini D (2024), 2024 IEEE International Conference on Robotics and Automation (ICRA), 16192-16198
BibTeX
@inproceedings{maskedssllearni-2024/8,
title={Masked γ-SSL: learning uncertainty estimation via masked image modeling},
author={Williams DSW, Gadd M, Newman P & De Martini D},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA 2024)},
pages={16192-16198},
year = "2024"
}
VDNA-PR: using general dataset representations for robust sequential visual place recognition
Ramtoula B, De Martini D, Gadd M & Newman P (2024), 2024 IEEE International Conference on Robotics and Automation (ICRA), 15883-15889
BibTeX
@inproceedings{vdnaprusinggene-2024/8,
title={VDNA-PR: using general dataset representations for robust sequential visual place recognition},
author={Ramtoula B, De Martini D, Gadd M & Newman P},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA 2024)},
pages={15883-15889},
year = "2024"
}
