Biography
Professor Nick Hawes completed a BSc (1999) and PhD (2004) in Artificial Intelligence (AI) at the University of Birmingham, before completing post-doctoral positions at MIT's Media Lab Europe in Dublin, and in the School of Computer Science at the University of Birmingham. From 2009, he led a research group around AI applied to robotics at Birmingham, progressing to the title of Reader in Autonomous Intelligent Robotics.
Nick moved to Oxford in September 2017, joining the Oxford Robotics Institute as an Associate Professor and Pembroke College as a Tutorial Fellow.
He became Director of the Oxford Robotics Institute within the Department of Engineering Science, in 2022.
Awards and Achievements
Nick was selected to give the Lord Kelvin Award Lecture at the 2013 British Science Festival. This honour is given to an active researcher who has demonstrated outstanding communication skills to a general audience.
Research Interests
Nick’s research interests lie in the application of Artificial Intelligence (AI) techniques to create intelligent, autonomous robots that can work with or for humans. He has worked on long-term autonomy for mobile robots; mixed initiative or shared autonomy between humans and robots; information-processing architectures for intelligent systems; the integration of AI planning techniques into a variety of robot systems; and the use of qualitative semantic and spatial representations to enable robots to reason about the possibilities for action in their worlds.
Research Groups
DPhil Opportunities
I am currently looking for DPhil students to join the GOALS lab at the Oxford Robotics Institute. DPhil topics will be in the area of long-term autonomy, the integration of learning and (probabilistic) planning, shared autonomy, or verification methods applied to robot behaviour.
Recent Publications
RAMBO-RL: robust adversarial model-based offline reinforcement learning
Rigter M, Lacerda B & Hawes N (2023), Advances in Neural Information Processing Systems 35 (NeurIPS 2022)
BibTeX
@inproceedings{ramborlrobustad-2023/4,
title={RAMBO-RL: robust adversarial model-based offline reinforcement learning},
author={Rigter M, Lacerda B & Hawes N},
booktitle={36th Conference on Neural Information Processing Systems (NeurIPS 2022)},
year = "2023"
}
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"
}
Bayesian reinforcement learning for single-episode missions in partially unknown environments
Budd M, Duckworth P, Hawes N & Lacerda B (2022), Proceedings of the 6th Conference on Robot Learning (CoRL 2022)
BibTeX
@inproceedings{bayesianreinfor-2022/10,
title={Bayesian reinforcement learning for single-episode missions in partially unknown environments},
author={Budd M, Duckworth P, Hawes N & Lacerda B},
booktitle={6th Conference on Robot Learning (CoRL 2022)},
year = "2022"
}
Shared autonomy systems with stochastic operator models
Costen C, Rigter M, Lacerda B & Hawes N (2022), Proceedings of the 31st International Joint Conferences on Artificial Intelligence Organization (IJCAI 2022), 4614-4620
Beta residuals: improving fault-tolerant control for sensory faults via bayesian inference and precision learning
Baioumy M, Hartemink W, Ferrari R & Hawes N (2022), IFAC-PapersOnLine, 55(6), 285-291
Time-bounded large-scale mission planning under uncertainty for UV disinfection
Brudermüller L, Bhattacharyya R, Lacerda B & Hawes N (2022)
BibTeX
@inproceedings{timeboundedlarg-2022/6,
title={Time-bounded large-scale mission planning under uncertainty for UV disinfection},
author={Brudermüller L, Bhattacharyya R, Lacerda B & Hawes N},
booktitle={PlanRob: ICAPS 2022 Workshop on Planning and Robotics},
year = "2022"
}
Planning for Risk-Aversion and Expected Value in MDPs
Rigter M, Duckworth P, Lacerda B & Hawes N (2022), Proceedings International Conference on Automated Planning and Scheduling, ICAPS, 32, 307-315
Context-aware modelling for multi-robot systems under uncertainty
Street C, Lacerda B, Staniaszek M, Mühlig M & Hawes N (2022), Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022)
BibTeX
@inproceedings{contextawaremod-2022/5,
title={Context-aware modelling for multi-robot systems under uncertainty},
author={Street C, Lacerda B, Staniaszek M, Mühlig M & Hawes N},
booktitle={21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2022},
year = "2022"
}
Flexible estimation of biodiversity with short-range multispectral imaging in a temperate grassland
Jackson J, Lawson CS, Adelmant C, Huhtala E, Fernandes P et al. (2022)
Risk-aware motion planning in partially known environments
Hawes N, Barbosa FS, Lacerda B, Duckworth P & Tumova J (2022), Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021), 5220-5226