Jackson Carter

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About

Jackson Carter’s research sits at the intersection of human perception and robotic manipulation, exploring how we can teach machines to grasp objects more intuitively by studying our own gaze and movement patterns. In his most cited work, “Evaluating human gaze patterns during grasping tasks” (2016, 3 citations), Carter uncovered key differences in how people visually guide their own hands versus a robotic hand, revealing that gaze shifts are more deliberate and predictive when controlling a machine. This insight has direct implications for designing more natural human-robot interfaces. His follow-up study, “Human-Planned Robotic Grasp Ranges: Capture and Validation” (2016, 2 citations), tackled the challenge of transferring human grasping skills to robots, identifying limitations in data capture efficiency and generalization. Though early in his career, Carter’s work is foundational for researchers aiming to build robots that learn from human demonstration. His focus on bridging perceptual psychology with robotics engineering offers a promising path toward more adaptive and human-aware automation, making him a rising voice in the field of human-robot interaction.