Diversity creation methods: a survey and categorisation G Brown, J Wyatt, R Harris, X Yao Information fusion 6 (1), 5-20, 2005 | 1323 | 2005 |
Managing diversity in regression ensembles. G Brown, JL Wyatt, P Tino Journal of machine learning research 6 (9), 2005 | 494 | 2005 |
The strands project: Long-term autonomy in everyday environments N Hawes, C Burbridge, F Jovan, L Kunze, B Lacerda, L Mudrova, J Young, ... IEEE Robotics & Automation Magazine 24 (3), 146-156, 2017 | 257 | 2017 |
Robot task planning and explanation in open and uncertain worlds M Hanheide, M Göbelbecker, GS Horn, A Pronobis, K Sjöö, A Aydemir, ... Artificial Intelligence 247, 119-150, 2017 | 191 | 2017 |
One-shot learning and generation of dexterous grasps for novel objects M Kopicki, R Detry, M Adjigble, R Stolkin, A Leonardis, JL Wyatt The International Journal of Robotics Research 35 (8), 959-976, 2016 | 155 | 2016 |
Functional object class detection based on learned affordance cues M Stark, P Lies, M Zillich, J Wyatt, B Schiele Computer Vision Systems: 6th International Conference, ICVS 2008 Santorini …, 2008 | 141 | 2008 |
Exploration and inference in learning from reinforcement J Wyatt University of Edinburgh. College of Science and Engineering. School of …, 1998 | 119 | 1998 |
Slurs, roles and power M Popa-Wyatt, JL Wyatt Philosophical Studies, 2017 | 115 | 2017 |
Modeling of deformable objects for robotic manipulation: A tutorial and review VE Arriola-Rios, P Guler, F Ficuciello, D Kragic, B Siciliano, JL Wyatt Frontiers in Robotics and AI 7, 82, 2020 | 109 | 2020 |
REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics M Sridharan, M Gelfond, S Zhang, J Wyatt Journal of Artificial Intelligence Research 65, 2019 | 104* | 2019 |
Towards an integrated robot with multiple cognitive functions N Hawes, A Sloman, J Wyatt, M Zillich, H Jacobsson, GJM Kruijff, ... AAAI 7, 1548-1553, 2007 | 87 | 2007 |
Learning to predict how rigid objects behave under simple manipulation M Kopicki, S Zurek, R Stolkin, T Mörwald, J Wyatt 2011 IEEE international conference on robotics and automation, 5722-5729, 2011 | 83 | 2011 |
Exploiting probabilistic knowledge under uncertain sensing for efficient robot behaviour M Hanheide, C Gretton, R Dearden, N Hawes, J Wyatt, A Pronobis, ... IJCAI Proceedings-International Joint Conference on Artificial Intelligence …, 2011 | 81 | 2011 |
Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs M Sridharan, J Wyatt, R Dearden Artificial Intelligence 174 (11), 704-725, 2010 | 78 | 2010 |
Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds S Zhang, M Sridharan, JL Wyatt IEEE Transactions on Robotics 31 (3), 699 - 713, 2015 | 72 | 2015 |
Two-level RRT planning for robotic push manipulation C Zito, R Stolkin, M Kopicki, JL Wyatt Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International …, 2012 | 67 | 2012 |
Mediating between qualitative and quantitative representations for task-orientated human-robot interaction. M Brenner, JD Kelleher, N Hawes, J Wyatt Technological University Dublin, 2007 | 65 | 2007 |
Cognitive systems HI Christensen, GJ Kruijff, JL Wyatt Springer Verlag, 2010 | 62* | 2010 |
Learning modular and transferable forward models of the motions of push manipulated objects M Kopicki, S Zurek, R Stolkin, T Moerwald, JL Wyatt Autonomous Robots 41 (5), 1061-1082, 2017 | 59 | 2017 |
Engineering intelligent information-processing systems with CAST N Hawes, J Wyatt Advanced Engineering Informatics 24 (1), 27-39, 2010 | 59 | 2010 |