Patric Hopfgarten
Papers
2
Total Citations
11
H-Index
2
About
Patric Hopfgarten is a robotics researcher whose work centers on sensor fusion, state estimation, and autonomous manipulation. His primary contributions lie in developing efficient algorithms for integrating data from multiple sensors in nonlinear systems, particularly when measurements arrive out of sequence—a common challenge in real-world mobile robotics. His 2020 paper on this topic, which has garnered 6 citations, provides a precise and computationally practical solution for improving state estimation accuracy in dynamic environments. Hopfgarten also contributed to the broader field of autonomous picking through his involvement in the Amazon Robotic Challenge, as documented in a 2018 paper with 5 citations. This work highlights progress in robotic grasping and manipulation under industrial constraints, showcasing his ability to bridge theoretical sensor fusion methods with applied robotics. Though his citation counts are modest, they reflect focused, early-career contributions that address critical bottlenecks in autonomous systems. His research is particularly relevant for students and engineers working on mobile robots, autonomous vehicles, or industrial automation, where reliable sensor integration and real-time decision-making are paramount.
Research Focus
Key Achievements
Top Papers
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