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About

Xiaoran Fan is a robotics researcher specializing in human-robot interaction, robot safety, and novel sensing technologies. His work addresses one of the field's most pressing challenges: enabling robots to safely perceive and respond to their environments, particularly in dynamic settings shared with humans. Fan's most influential contribution, "Enabling Low-Cost Full Surface Tactile Skin for Human Robot Interaction" (2022, 31 citations), tackles the longstanding engineering dream of full-coverage tactile sensing on robotic surfaces, making it practically achievable through cost-effective design. Complementing this, his acoustic sensing research has opened innovative pathways for collision detection: his 2020 paper on acoustic collision detection (22 citations) demonstrated how sound signatures from robot-object contact can enable rapid safety responses, while AuraSense (2021, 20 citations) extended proximity detection across an entire robot surface to prevent collisions before they occur. His more recent AmbiSense system (2023) further refines this acoustic approach, achieving blindspot-free proximity detection and bearing estimation using a single inexpensive piezoelectric transducer. Collectively, Fan's research demonstrates a consistent drive toward practical, low-cost sensing solutions that make collaborative robotics safer and more accessible, accumulating over 80 citations across his published work.