Papers
3
Total Citations
10
H-Index
2
About
Yasen Wang is a robotics researcher whose work focuses on advancing the autonomy and precision of industrial and field robots, with key contributions in sensor calibration, dynamics identification, and tactile manipulation. Wang’s most cited paper, “SDI: A sparse drift identification approach for force/torque sensor calibration in industrial robots” (2024, 6 citations), introduces a novel method to correct sensor drift, enabling more accurate force feedback for high-precision manufacturing tasks. In “A two‑stage Bayesian framework for rapid dynamics identification in industrial robots” (2024, 1 citation), Wang developed a probabilistic framework that allows robots to update their dynamic models in real time, improving adaptability for model‑based control and disturbance estimation. A particularly innovative contribution is the “Robotic Tactile Excavation System” (2025, 3 citations), which uses an underactuated tactile finger to explore and excavate objects buried in granular materials without visual cues—a breakthrough for search‑and‑rescue and planetary exploration. With a growing citation record and work spanning calibration, real‑time modeling, and tactile sensing, Wang is establishing a reputation for practical, data‑driven solutions that push the boundaries of robotic dexterity and reliability in unstructured environments.
Research Focus
Key Achievements
Top Papers
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