Biography
Nick is a Professor of Artificial Intelligence and Robotics and a Tutorial Fellow in Engineering Science at Pembroke College. He joined the Oxford Robotics Institute in September 2017. Previously he was a Reader in Autonomous Intelligent Robotics in the School of Computer Science at the University of Birmingham, where he also completed a BSc and PhD in Artificial Intelligence. For more information see: http://nickhaw.es
Most Recent Publications
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
Negotiated Path Planning for Non-Cooperative Multi-Robot Systems
Gautier A, Stephens A, Lacerda B, Hawes N & Wooldridge M (2022), Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 1, 472-480
Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning
Baioumy M, Hartemink W, Ferrari RMG & Hawes N (2022), IFAC-PapersOnLine, 55(6), 285-291
Decision-making under uncertainty for multi-robot systems
Lacerda B, Gautier A, Rutherford A, Stephens A, Street C et al. (2022), AI COMMUNICATIONS, 35(4), 433-441
Efficiently exploring for human robot interaction: partially observable Poisson processes
Jovan F, Tomy M, Hawes N & Wyatt J (2022), AUTONOMOUS ROBOTS
Research
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.
From 2013 to 2017 Nick was the coordinator of the STRANDS project (Spatio-Temporal Representations and Activities for Cognitive Control in Long-Term Scenarios), a 4-year EU FP7 Integrating Project. Please see the project’s website, the STRANDS overview paper, or the “Success Stories of FP7” presentation at the European Robotics Forum 2017.
Nick has given invited talks to many conferences, workshops, summer schools. Follow the links to watch Nick’s talk on learning and semantic representations to the 2016 WASP graduate school, and a tutorial on probabilistic planning for mobile robots at the 4th Lucia School on AI and Robotics.
Most Recent Publications
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
Negotiated Path Planning for Non-Cooperative Multi-Robot Systems
Gautier A, Stephens A, Lacerda B, Hawes N & Wooldridge M (2022), Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 1, 472-480
Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning
Baioumy M, Hartemink W, Ferrari RMG & Hawes N (2022), IFAC-PapersOnLine, 55(6), 285-291
Decision-making under uncertainty for multi-robot systems
Lacerda B, Gautier A, Rutherford A, Stephens A, Street C et al. (2022), AI COMMUNICATIONS, 35(4), 433-441
Efficiently exploring for human robot interaction: partially observable Poisson processes
Jovan F, Tomy M, Hawes N & Wyatt J (2022), AUTONOMOUS ROBOTS
Publications
See Nick’s personal website or Google Scholar profile.
Most Recent Publications
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
Negotiated Path Planning for Non-Cooperative Multi-Robot Systems
Gautier A, Stephens A, Lacerda B, Hawes N & Wooldridge M (2022), Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 1, 472-480
Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning
Baioumy M, Hartemink W, Ferrari RMG & Hawes N (2022), IFAC-PapersOnLine, 55(6), 285-291
Decision-making under uncertainty for multi-robot systems
Lacerda B, Gautier A, Rutherford A, Stephens A, Street C et al. (2022), AI COMMUNICATIONS, 35(4), 433-441
Efficiently exploring for human robot interaction: partially observable Poisson processes
Jovan F, Tomy M, Hawes N & Wyatt J (2022), AUTONOMOUS ROBOTS
