Papers
1
Total Citations
14
H-Index
1
About
Pete Fan’s research lies at the intersection of robotics, human-machine interaction, and cognitive engineering, with a focus on reducing the cognitive burden on teleoperators during complex contact tasks. His most cited work, “Reducing the Teleoperator’s Cognitive Burden for Complex Contact Tasks Using Affordance Primitives” (2020, 14 citations), introduces a novel framework that leverages affordance templates and selective compliance to enable robotic manipulators to handle both known tasks—where uncertainty complicates execution—and unknown tasks lacking predefined motion waypoints or force profiles. By integrating affordance primitives, Fan’s approach streamlines real-world remote manipulation, allowing operators to focus on high-level decision-making rather than micromanaging every joint movement. This contribution is particularly impactful for applications in hazardous environments, such as nuclear decommissioning or space exploration, where precise contact tasks are critical. With 14 citations, this paper underscores Fan’s ability to address practical challenges in teleoperation, blending theory with deployable solutions. His work stands out for its emphasis on cognitive load reduction, a key barrier in scaling robotic assistance. For students and researchers, Fan’s research offers a compelling pathway into designing more intuitive, human-centered robotic systems that bridge the gap between autonomy and operator control.
Research Focus
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Top Papers
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