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
DITTO: Offline Imitation Learning with World Models
DeMoss B, Duckworth P, Hawes N & Posner I (2023)
BibTeX
@misc{dittoofflineimi-2023/2,
title={DITTO: Offline Imitation Learning with World Models},
author={DeMoss B, Duckworth P, Hawes N & Posner I},
year = "2023"
}
You Only Look at One: Category-Level Object Representations for Pose Estimation From a Single Example
Goodwin W, Havoutis I & Posner I (2023), Proceedings of Machine Learning Research, 205, 1435-1445
BibTeX
@inproceedings{youonlylookaton-2023/1,
title={You Only Look at One: Category-Level Object Representations for Pose Estimation From a Single Example},
author={Goodwin W, Havoutis I & Posner I},
pages={1435-1445},
year = "2023"
}
Leveraging Scene Embeddings for Gradient-Based Motion Planning in Latent Space
Yamada J, Hung CM, Collins J, Havoutis I & Posner I (2023), Proceedings - IEEE International Conference on Robotics and Automation, 2023-May, 5674-5680
BibTeX
@inproceedings{leveragingscene-2023/1,
title={Leveraging Scene Embeddings for Gradient-Based Motion Planning in Latent Space},
author={Yamada J, Hung CM, Collins J, Havoutis I & Posner I},
pages={5674-5680},
year = "2023"
}
VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation
Mitchell AL, Merkt WX, Geisert M, Gangapurwala S, Engelcke M et al. (2023), IEEE Transactions on Robotics, 1-16
BibTeX
@article{vaelocoversatil-2023/,
title={VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation},
author={Mitchell AL, Merkt WX, Geisert M, Gangapurwala S, Engelcke M et al.},
journal={IEEE Transactions on Robotics},
pages={1-16},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
year = "2023"
}
Zero-shot category-level object pose estimation
Goodwin W, Vaze S, Havoutis I & Posner I (2022)(13699), 516-532
BibTeX
@inproceedings{zeroshotcategor-2022/10,
title={Zero-shot category-level object pose estimation},
author={Goodwin W, Vaze S, Havoutis I & Posner I},
booktitle={17th European Conference on Computer Vision (ECCV 2022)},
pages={516-532},
year = "2022"
}
Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation
Hung C-M, Zhong S, Goodwin W, Jones OP, Engelcke M et al. (2022)
BibTeX
@misc{reachingthrough-2022/10,
title={Reaching Through Latent Space: From Joint Statistics to Path Planning in
Manipulation},
author={Hung C-M, Zhong S, Goodwin W, Jones OP, Engelcke M et al.},
year = "2022"
}
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"
}
ObPose: Leveraging Pose for Object-Centric Scene Inference in 3D
Wu Y, Jones OP & Posner I (2022)
BibTeX
@misc{obposeleveragin-2022/6,
title={ObPose: Leveraging Pose for Object-Centric Scene Inference in 3D},
author={Wu Y, Jones OP & Posner I},
year = "2022"
}
