Daniele De Martini

Source: https://ori.ox.ac.uk/people/daniele-de-martini Parent: https://ori.ox.ac.uk/news/rethinking-robotics-from-intelligent-machines-to-intelligent-environments

EMAIL: daniele@robots.ox.ac.uk

Biography

Publications

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

Robot-relay: building-wide, calibration-less visual servoing with learned sensor handover networks

Robinson L, Gadd M, Newman P & Martini D (2026), Autonomous Robots, 50(1)

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BibTeX

@inproceedings{robotrelaybuild-2026/3,
  title={Robot-relay: building-wide, calibration-less visual servoing with learned sensor handover networks},
  author={Robinson L, Gadd M, Newman P & Martini D},
  year = "2026"
}

Biodiversity research requires more motors in air, water and on land

Qi M, Gadd M, De Martini D, Davis KJ, Xiong B et al. (2025), Methods in Ecology and Evolution

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BibTeX

@article{biodiversityres-2025/12,
  title={Biodiversity research requires more motors in air, water and on land},
  author={Qi M, Gadd M, De Martini D, Davis KJ, Xiong B et al.},
  journal={Methods in Ecology and Evolution},
  number={2041-210x.70217},
  publisher={Wiley},
  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)

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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)

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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

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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

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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"
}

MinkOcc: Towards real-time label-efficient semantic occupancy prediction

Sze S, De Martini D & Kunze L (2025)

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BibTeX

@misc{minkocctowardsr-2025/4,
  title={MinkOcc: Towards real-time label-efficient semantic occupancy prediction},
  author={Sze S, De Martini D & Kunze L},
  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)

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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"
}

Tiny Lidars for Manipulator Self-Awareness: Sensor Characterization and Initial Localization Experiments

Caroleo G, Albini A, De Martini D, Barfoot TD & Maiolino P (2025)

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BibTeX

@inproceedings{tinylidarsforma-2025/3,
  title={Tiny Lidars for Manipulator Self-Awareness: Sensor Characterization and Initial Localization Experiments},
  author={Caroleo G, Albini A, De Martini D, Barfoot TD & Maiolino P},
  booktitle={IEEE/RSJ International Conference  on Intelligent Robots and Systems},
  year = "2025"
}

Select2Plan: Training-Free ICL-Based Planning Through VQA and Memory Retrieval

Buoso D, Robinson L, Averta G, Torr P, Franzmeyer T et al. (2025), IEEE Robotics and Automation Letters, 10(11), 11267-11274

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BibTeX

@article{selectplantrain-2025/1,
  title={Select2Plan: Training-Free ICL-Based Planning Through VQA and Memory Retrieval},
  author={Buoso D, Robinson L, Averta G, Torr P, Franzmeyer T et al.},
  journal={IEEE Robotics and Automation Letters},
  volume={10},
  pages={11267-11274},
  year = "2025"
}

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