Papers
10
Total Citations
164
H-Index
8
About
N. Taylor is a pioneering roboticist whose research spans robot motion planning, autonomous fault diagnosis, and human-robot collaboration. Taylor’s most influential work introduced ant colony optimization (ACO) to robot motion planning, combining it with probabilistic roadmap planners to solve complex path-planning problems for articulated and 6-DOF robots—a contribution that has garnered over 48 citations across multiple papers. In fault diagnosis, Taylor developed the RECOVERY system, an integrated heterogeneous-knowledge approach that significantly enhanced mission robustness for autonomous robotic vehicles, earning 28 citations. Taylor also advanced robotic manipulation with adaptable pouring systems, using fast but approximate fluid simulation to teach robots not to spill (29 citations), and later refined liquid property calibration for pouring actions (15 citations). Notable achievements include organizing the 1st Annual Conference on Robot Learning (CoRL 2017) and proposing a hierarchical attention-based neural network architecture inspired by human brain guidance for perception and reasoning. With over 160 total citations, Taylor’s work bridges classical optimization, soft robotics, and cognitive architectures, leaving a lasting impact on both industrial and research robotics.
Research Focus
Key Achievements
Top Papers
- 1Ant Colony Robot Motion Planning32 citations · 2005
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- 4Articulated Robot Motion Planning Using Ant Colony Optimisation16 citations · 2006
- 5Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions15 citations · 2020
- 6The 1st Annual Conference on Robot Learning (CoRL 2017)14 citations · 2017
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- 8Energy‐Based Abstraction for Soft Robotic System Development9 citations · 2021
- 9Human Robot Collaboration in Production Environments5 citations · 2014
- 10Planning robot installations by CAD2 citations · 1982