World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
6278
World Ranking
9782
National Ranking
614

Overview

Ingmar Posner is affiliated with the University of Oxford in the United Kingdom. Their research primarily spans the fields of Computer Science and Engineering, with a significant focus on subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Biomedical Engineering, and Aerospace Engineering.

Posner's work covers several main topics, including:

  • Robot Manipulation and Learning
  • Human Pose and Action Recognition
  • Robotics and Sensor-Based Localization
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Robotic Locomotion and Control
  • Reinforcement Learning in Robotics

Frequent collaborators include Ioannis Havoutis, Ōiwi Parker Jones, Martin Engelcke, Alex Mitchell, and Jun Yamada. These recurring partnerships reflect a sustained engagement with research communities involved in robotics and machine learning.

Posner has published extensively, with notable papers including:

  • "From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence," 2021, arXiv (Cornell University)
  • "Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement," 2022, 2022 International Conference on Robotics and Automation (ICRA)
  • "Fast-MbyM: Leveraging Translational Invariance of the Fourier Transform for Efficient and Accurate Radar Odometry," 2022, 2022 International Conference on Robotics and Automation (ICRA)
  • "RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces," 2020, arXiv (Cornell University)
  • "GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement," 2021, arXiv (Cornell University)

The most frequent publication venues for their work are arXiv (Cornell University), IEEE Robotics and Automation Letters, the 2022 International Conference on Robotics and Automation (ICRA), IEEE Access, and IEEE Transactions on Robotics.

Best Publications

  • Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks

    Martin Engelcke;Dushyant Rao;Dominic Zeng Wang;Chi Hay Tong

  • The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset

    Dan Barnes;Matthew Gadd;Paul Murcutt;Paul Newman

  • Maximum Entropy Deep Inverse Reinforcement Learning

    Markus Wulfmeier;Peter Ondruska;Ingmar Posner

  • Voting for Voting in Online Point Cloud Object Detection

    Dominic Zeng Wang;Ingmar Posner

  • Deep tracking: seeing beyond seeing using recurrent neural networks

    Peter Ondrúška;Ingmar Posner

  • Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers

    Paul Newman;Gabe Sibley;Mike Smith;Mark Cummins

  • Large-scale cost function learning for path planning using deep inverse reinforcement learning

    Markus Wulfmeier;Dushyant Rao;Dominic Zeng Wang;Peter Ondruska

  • Toward automated driving in cities using close-to-market sensors: An overview of the V-Charge Project

    Paul Furgale;Ulrich Schwesinger;Martin Rufli;Wojciech Derendarz

  • Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects

    Adam R. Kosiorek;Hyunjik Kim;Yee Whye Teh;Ingmar Posner

  • What could move? Finding cars, pedestrians and bicyclists in 3D laser data

    Dominic Zeng Wang;Ingmar Posner;Paul Newman

  • Watch this: Scalable cost-function learning for path planning in urban environments

    Markus Wulfmeier;Dominic Zeng Wang;Ingmar Posner

  • Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects.

    Adam R. Kosiorek;Hyunjik Kim;Ingmar Posner;Yee Whye Teh

  • Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy

    Dan Barnes;Will Maddern;Ingmar Posner

  • Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar

    Dan Barnes;Ingmar Posner

  • Deep tracking in the wild: End-to-end tracking using recurrent neural networks:

    Julie Dequaire;Peter Ondruska;Dushyant Rao;Dominic Zeng Wang

  • Incremental Adversarial Domain Adaptation for Continually Changing Environments

    Markus Wulfmeier;Alex Bewley;Ingmar Posner

  • Model-free detection and tracking of dynamic objects with 2D lidar

    Dominic Zeng Wang;Ingmar Posner;Paul Newman

  • On the Limitations of Representing Functions on Sets

    Edward Wagstaff;Fabian B. Fuchs;Martin Engelcke;Ingmar Posner

  • A generative framework for fast urban labeling using spatial and temporal context

    Ingmar Posner;Mark Cummins;Paul Newman

  • Automated valet parking and charging for e-mobility

    Ulrich Schwesinger;Mathias Burki;Julian Timpner;Stephan Rottmann

  • GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations

    Martin Engelcke;Adam R. Kosiorek;Oiwi Parker Jones;Ingmar Posner

  • ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking

    Oliver Groth;Fabian B. Fuchs;Ingmar Posner;Andrea Vedaldi

Frequent Co-Authors

Paul Newman
Paul Newman University of Oxford
Andrea Vedaldi
Andrea Vedaldi University of Oxford
Marta Kwiatkowska
Marta Kwiatkowska University of Oxford
Raia Hadsell
Raia Hadsell DeepMind (United Kingdom)
Max Welling
Max Welling University of Amsterdam
Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Lars Wolf
Lars Wolf Technische Universität Braunschweig
Peter Corke
Peter Corke Queensland University of Technology

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens up a world of flexible study and career options. Many students are now looking at 1 year associate degree programs online as a quick entry point to the tech industry. These programs provide foundational knowledge and can lead to junior roles or further study.

For those aiming for advanced positions, choosing the most worthwhile masters degrees can make a big difference in employability and earning potential. There are also options for accelerated learning, with the shortest masters degree programs online allowing professionals to upskill quickly without putting their careers on hold.

If you want to boost your qualifications without committing to a full degree, there are short certificate programs that pay well—perfect for skills upgrades or moving into a new tech role fast. No matter your background or career goals, there are numerous online study paths that can help you achieve your ambitions in computer science.

Best Scientists Citing Ingmar Posner

Trending Scientists

Recently Published Articles