Papers
8
Total Citations
154
H-Index
6
About
Shaolin Zhang is a robotics researcher whose work spans sensorless hand guiding, tactile sensing, and autonomous task planning for industrial and service robots. His most-cited paper (79 citations) introduces a sensorless hand guiding scheme that enables industrial robots to be taught by hand without force sensors, minimizing external force estimation errors—a practical contribution to intuitive human-robot collaboration. Zhang’s work on GelStereo tactile sensing (20 citations) provides high-resolution contact geometry for dexterous manipulation, including slip detection, while his GelStereo BioTip (9 citations) presents a self-calibrating, biomimetic visuotactile fingertip sensor that overcomes the bulkiness and flat-contact limitations of prior designs. He has also advanced reactive task planning with large language models (13 citations), enabling robots to adapt to dynamic environments without predefined rules. Additional contributions include dynamic calibration for multi-joint robots and ontology-based autonomous task processing. Zhang’s research demonstrates a clear trajectory from foundational sensing and control to intelligent, adaptive robotic systems, with growing impact in both industrial and research communities.
Research Focus
Key Achievements
Top Papers
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- 4Text2Reaction : Enabling Reactive Task Planning Using Large Language Models13 citations · 2024
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- 6Ontology based autonomous robot task processing framework6 citations · 2024
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