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About

Yuchen Mo is a robotics researcher whose work sits at the intersection of tactile perception, human-robot interaction, and autonomous manipulation. Mo’s research focuses on enabling robots to perceive and interact with the physical world in more human-like ways—through touch, language, and adaptive feedback. Their most cited work, “Active Clothing Material Perception Using Tactile Sensing and Deep Learning” (138 citations), demonstrates how robots can autonomously discriminate object properties like fabric texture by combining tactile sensing with deep learning, a key step toward dexterous manipulation of deformable objects. Mo has also advanced interactive visual grounding for robotic grasping, introducing SeeAsk, an open-world system that resolves ambiguous natural language instructions during grasping tasks. More recently, they contributed InViG, a large-scale benchmark dataset with 500K dialogues designed to evaluate open-ended interactive grounding in human-robot communication, and DoorBot, a closed-loop system that uses haptic feedback for robust door opening in unstructured environments. Through these contributions, Mo is helping to build robots that can understand both the physical and communicative nuances of everyday tasks—a critical frontier in embodied AI.