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
Reinforcement Learning for Bandits with Continuous Actions and Large Context Spaces
Duckworth P, Vallis KA, Lacerda B & Hawes N (2023), Frontiers in Artificial Intelligence and Applications, 372, 590-597
BibTeX
@inproceedings{reinforcementle-2023/9,
title={Reinforcement Learning for Bandits with Continuous Actions and Large Context Spaces},
author={Duckworth P, Vallis KA, Lacerda B & Hawes N},
pages={590-597},
year = "2023"
}
Experimental drought reduces the productivity and stability of a recovering calcareous grassland
Jackson J, Middleton SL, Lawson CS, Jardine E, Hawes N et al. (2023)
BibTeX
@misc{experimentaldro-2023/7,
title={Experimental drought reduces the productivity and stability of a recovering calcareous grassland},
author={Jackson J, Middleton SL, Lawson CS, Jardine E, Hawes N et al.},
year = "2023"
}
Planning with Hidden Parameter Polynomial MDPs
Costen C, Rigter M, Lacerda B & Hawes N (2023), Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, 37, 11963-11971
BibTeX
@inproceedings{planningwithhid-2023/6,
title={Planning with Hidden Parameter Polynomial MDPs},
author={Costen C, Rigter M, Lacerda B & Hawes N},
pages={11963-11971},
year = "2023"
}
Multi-Unit Auctions for Allocating Chance-Constrained Resources
Gautier A, Lacerda B, Hawes N & Wooldridge M (2023), Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, 37, 11560-11568
BibTeX
@inproceedings{multiunitauctio-2023/6,
title={Multi-Unit Auctions for Allocating Chance-Constrained Resources},
author={Gautier A, Lacerda B, Hawes N & Wooldridge M},
pages={11560-11568},
year = "2023"
}
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 (2023), Proceedings of Machine Learning Research, 205, 1189-1198
BibTeX
@inproceedings{bayesianreinfor-2023/1,
title={Bayesian Reinforcement Learning for Single-Episode Missions in Partially Unknown Environments},
author={Budd M, Duckworth P, Hawes N & Lacerda B},
pages={1189-1198},
year = "2023"
}
VP-STO: Via-point-based Stochastic Trajectory Optimization for Reactive Robot Behavior
Jankowski J, Brudermuller L, Hawes N & Calinon S (2023), Proceedings - IEEE International Conference on Robotics and Automation, 2023-May, 10125-10131
BibTeX
@inproceedings{vpstoviapointba-2023/1,
title={VP-STO: Via-point-based Stochastic Trajectory Optimization for Reactive Robot Behavior},
author={Jankowski J, Brudermuller L, Hawes N & Calinon S},
pages={10125-10131},
year = "2023"
}
Risk-Constrained Planning for Multi-Agent Systems with Shared Resources
Gautier A, Rigter M, Lacerda B, Hawes N & Wooldridge M (2023), Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2023-May, 113-121
BibTeX
@inproceedings{riskconstrained-2023/1,
title={Risk-Constrained Planning for Multi-Agent Systems with Shared Resources},
author={Gautier A, Rigter M, Lacerda B, Hawes N & Wooldridge M},
pages={113-121},
year = "2023"
}
Correction: Efficiently exploring for human robot interaction: partially observable Poisson processes (Autonomous Robots, (2023), 47, 1, (121-138), 10.1007/s10514-022-10070-9)
Jovan F, Tomy M, Hawes N & Wyatt J (2023), Autonomous Robots
BibTeX
@article{correctioneffic-2023/1,
title={Correction: Efficiently exploring for human robot interaction: partially observable Poisson processes (Autonomous Robots, (2023), 47, 1, (121-138), 10.1007/s10514-022-10070-9)},
author={Jovan F, Tomy M, Hawes N & Wyatt J},
journal={Autonomous Robots},
year = "2023"
}
