Papers
19
Total Citations
357
H-Index
9
About
Jesse Haviland is a robotics researcher whose work spans robot motion control, kinematics, and the emerging intersection of large language models with robotic planning. He is perhaps best known for his pivotal role in reimagining the Robotics Toolbox for Python (2021), a successor to the widely used MATLAB toolbox that has shaped robotics education and research for over two decades — earning more than 100 citations in just a few years. His NEO algorithm (2021, 71 citations) established him as a leading voice in reactive motion control, offering a fast, obstacle-aware controller that simultaneously optimizes manipulability and respects joint constraints. Haviland has also made foundational contributions to manipulator kinematics pedagogy through his two-part tutorial series on differential kinematics, and to computational rigor through his work on the Elementary Transform Sequence. More recently, he has pushed boundaries at the frontier of AI-driven robotics, contributing SayPlan (2023), which grounds large language models in 3D scene graphs for scalable task planning, and Bayesian Controller Fusion, which elegantly blends classical control priors with deep reinforcement learning. His body of work reflects a rare ability to bridge rigorous mathematical foundations with practical, real-world robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Not your grandmother’s toolbox – the Robotics Toolbox reinvented for Python102 citations · 2021
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- 4Visibility Maximization Controller for Robotic Manipulation25 citations · 2022
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- 8A Purely-Reactive Manipulability-Maximising Motion Controller18 citations · 2020
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