Papers
5
Total Citations
39
H-Index
4
About
Jacob J. Johnson is a researcher at the intersection of robotics and neuroscience, whose work advances both autonomous systems and our understanding of the human brain. His primary contributions lie in motion planning for robots, where he has pioneered learning-based methods to overcome the scalability limitations of traditional sampling-based planners. Johnson introduced the Motion Planning Transformers (MPT) framework, which leverages transformer architectures to learn efficient sampling dictionaries, enabling faster and more generalizable planning for mobile robots and non-holonomic systems. His 2023 paper on this topic has already garnered 19 citations, reflecting its impact on the field. In parallel, Johnson explores the neural underpinnings of motor control, investigating how nonmotor brain regions encode path-related information during movement. His studies, cited a combined 11 times, challenge conventional views by highlighting the role of associative cortical areas in motor tasks, with implications for advanced neural prosthetics. By bridging robotics and neuroscience, Johnson is shaping more intelligent, adaptable systems and deepening our understanding of sensorimotor integration.
Research Focus
Key Achievements
Top Papers
- 1
- 2Nonmotor regions encode path-related information during movements6 citations · 2017
- 3
- 4The role of nonmotor brain regions during human motor control5 citations · 2017
- 5