Chen-Ting Wen

Papers

Total Citations

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About

Chen-Ting Wen is a robotics researcher advancing tactile sensing and manipulation for dexterous robotic systems. Their primary research areas include tactile servoing, pressure distribution control, and deep learning for robotic perception. Wen’s major contribution lies in pioneering tactile servoing schemes that enable robots to adjust grip and interaction forces based on real-time tactile feedback, moving beyond traditional visual or force-based control. Their most cited work, "Tactile Servoing Based Pressure Distribution Control of a Manipulator Using a Convolutional Neural Network" (2021, 9 citations), demonstrates how a CNN—specifically LeNet-5—significantly outperforms conventional tactile Jacobian methods in regulating pressure distribution during manipulation. This innovation enhances robots’ ability to handle delicate or deformable objects with human-like precision. Wen’s foundational paper, "Tactile Servo Based on Pressure Distribution" (2019, 3 citations), established the core framework for integrating tactile feedback into closed-loop control. By bridging machine learning and tactile sensing, Wen’s work addresses critical challenges in adaptive grasping and safe human-robot interaction. Their research holds promise for applications in manufacturing, healthcare, and assistive robotics, where nuanced touch feedback is essential.