Astrid Jackson

Toyota Research Institute, University of Central Florida

Papers

Total Citations

H-Index

About

Astrid Jackson is a researcher at the forefront of human-robot interaction, with a primary focus on making robot training more intuitive and accessible. Her work centers on the intersection of immersive technologies and machine learning, specifically exploring how virtual reality (VR) can enhance the process of teaching robots through demonstration. Jackson’s key contributions demonstrate that VR-based demonstrations can reduce the artifacts and biases introduced by traditional interfaces, allowing for cleaner, more transferable training data. Her 2019 paper, "The Benefits of Immersive Demonstrations for Teaching Robots," has garnered 8 citations, while her foundational 2018 work on VR demonstrations has been cited 5 times, establishing her as a voice in this niche. Earlier, Jackson tackled the challenge of reinforcement learning for high-degree-of-freedom humanoid robots, proposing continuous state/action models to overcome data scarcity—a problem she addressed in her 2016 paper, which has earned 2 citations. Though her citation counts are modest, her work is notable for bridging the gap between user-friendly interfaces and complex robotic control, offering a promising path toward more natural, human-centric robot training.