Daniele De Martini is Associate Professor in Mobile Robotics at the Oxford Robotics Institute and the Oxford e-Research Centre, University of Oxford, and Tutorial Fellow in Engineering Science at Keble College. He co-leads the Mobile Robotics Group alongside Professor Paul Newman. His research combines robotics and artificial intelligence to design autonomous systems capable of perception, mapping, localisation, and adaptive decision-making. By exploiting multiple sensing modalities—from vision to lidar to radar—his group explores how robots can reliably navigate and interpret the world, even in challenging or extreme conditions. Daniele has deployed robots in environments ranging from urban Oxford to the Scottish Highlands. Another key aspect of his work examines robotics–infrastructure interaction, enabling dynamic sharing of sensing and computing resources between robots and smart environments to support safe and scalable autonomy.
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"
}
Biodiversity research requires more motors in air, water and on land
QI MAN, Gadd M, DE MARTINI D, Davis K, Xiong B et al. (2025), Methods in Ecology and Evolution
BibTeX
@article{biodiversityres-2025/12,
title={Biodiversity research requires more motors in air, water and on land},
author={QI MAN, Gadd M, DE MARTINI D, Davis K, Xiong B et al.},
journal={Methods in Ecology and Evolution},
publisher={Wiley},
year = "2025"
}
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"
}
MinkOcc: towards real-time label-efficient semantic occupancy prediction
Sze S, De Martini D & Kunze L (2025), 4036-4043
BibTeX
@inproceedings{minkocctowardsr-2025/11,
title={MinkOcc: towards real-time label-efficient semantic occupancy prediction},
author={Sze S, De Martini D & Kunze L},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025)},
pages={4036-4043},
year = "2025"
}
Ensemble of pre-trained models for long-tailed trajectory prediction
Thuremella D, Yang Y, Wanna S, Kunze L & De Martini D (2025), 2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), 2055-2062
BibTeX
@inproceedings{ensembleofpretr-2025/11,
title={Ensemble of pre-trained models for long-tailed trajectory prediction},
author={Thuremella D, Yang Y, Wanna S, Kunze L & De Martini D},
booktitle={IEEE International Conference on Intelligent Transportation Systems (ITSC 2025)},
pages={2055-2062},
year = "2025"
}
LikePhys: Evaluating Intuitive Physics Understanding in Video Diffusion Models via Likelihood Preference
Yuan J, Pizzati F, Pinto F, Kunze L, Laptev I et al. (2025)
BibTeX
@misc{likephysevaluat-2025/10,
title={LikePhys: Evaluating Intuitive Physics Understanding in Video Diffusion Models via Likelihood Preference},
author={Yuan J, Pizzati F, Pinto F, Kunze L, Laptev I et al.},
year = "2025"
}
Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
Thuremella D, Yang Y, Wanna S, Kunze L & De Martini D (2025)
BibTeX
@misc{ensembleofpretr-2025/9,
title={Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction},
author={Thuremella D, Yang Y, Wanna S, Kunze L & De Martini D},
year = "2025"
}
AutoInspect: towards long-term autonomous inspection and monitoring
Staniaszek M, Flatscher T, Rowell J, Niu H, Liu W et al. (2025), IEEE Transactions on Field Robotics
BibTeX
@article{autoinspecttowa-2025/7,
title={AutoInspect: towards long-term autonomous inspection and monitoring},
author={Staniaszek M, Flatscher T, Rowell J, Niu H, Liu W et al.},
journal={IEEE Transactions on Field Robotics},
publisher={IEEE},
year = "2025"
}
The Oxford RobotCycle Project: A Multimodal Urban Cycling Dataset for Assessing the Safety of Vulnerable Road Users
Panagiotaki E, Thuremella D, Baghabrah J, Sze S, Fu LFT et al. (2025), IEEE Transactions on Field Robotics, PP(99), 1-1
BibTeX
@article{theoxfordrobotc-2025/5,
title={The Oxford RobotCycle Project: A Multimodal Urban Cycling Dataset for Assessing the Safety of Vulnerable Road Users},
author={Panagiotaki E, Thuremella D, Baghabrah J, Sze S, Fu LFT et al.},
journal={IEEE Transactions on Field Robotics},
volume={PP},
pages={1-1},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
year = "2025"
}
