Charles Gretton
Papers
4
Total Citations
238
H-Index
4
About
Charles Gretton is a leading researcher in artificial intelligence and robotics, whose work focuses on enabling autonomous systems to operate intelligently under uncertainty. His primary research areas include robot task planning, probabilistic reasoning, and automated explanation generation. Gretton’s most significant contribution is his pioneering work on robot task planning and explanation in open and uncertain worlds (152 citations), which addresses the critical challenge of making robot behavior both efficient and interpretable in dynamic environments. He has also made substantial advances in exploiting probabilistic knowledge for robot behavior under uncertain sensing (46 citations), developing methods that allow robots to use commonsense knowledge to improve reliability. His work on switching planners for combined task and observation planning (33 citations) provides lightweight, robust solutions for practical mobile robot control. Additionally, Gretton has contributed to topological environment mapping (7 citations), addressing perceptual aliasing challenges without relying on odometric information. His research has been widely cited and has influenced the development of more capable, trustworthy autonomous systems that can explain their decisions and adapt to real-world complexity.
Research Focus
Key Achievements
Top Papers
- 1Robot task planning and explanation in open and uncertain worlds152 citations · 2015
- 2Exploiting Probabilistic Knowledge under Uncertain Sensing for Efficient Robot Behaviour46 citations · 2011
- 3A Switching Planner for Combined Task and Observation Planning33 citations · 2011
- 4