Daniel Fernandes Gomes
Papers
11
Total Citations
618
H-Index
9
About
Daniel Fernandes Gomes is a robotics researcher whose work sits at the intersection of tactile sensing, robotic manipulation, and Sim2Real learning. He is best known for developing innovative optical tactile sensors that extend perception capabilities across the full surface of robotic fingers, addressing a critical limitation in how robots interact with complex environments. His most celebrated contribution, the **GelTip** sensor — a finger-shaped optical tactile sensor — has garnered nearly 400 citations across its associated publications, establishing it as a landmark design in the field. Gomes has also pioneered methods for generating synthetic tactile images to bridge the simulation-to-reality gap, enabling robots trained in virtual environments to perform effectively in the real world. His **TouchRoller** sensor expanded tactile assessment to large surface areas, while **RoTipBot** demonstrated sophisticated handling of thin, flexible objects using rotatable tactile sensors. Beyond manipulation, his research extends to infrastructure inspection, applying active tactile perception to crack detection in challenging environments. With over 600 cumulative citations, Gomes has meaningfully advanced the field's understanding of how richer tactile feedback can make robots more capable, adaptable, and practical across real-world scenarios.
Research Focus
Key Achievements
Top Papers
- 12020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)285 citations · 2020
- 2GelTip: A Finger-shaped Optical Tactile Sensor for Robotic Manipulation109 citations · 2020
- 3Generation of GelSight Tactile Images for Sim2Real Learning87 citations · 2021
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- 7Reducing Tactile Sim2Real Domain Gaps via Deep Texture Generation Networks18 citations · 2022
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- 9GelTip tactile sensor for dexterous manipulation in clutter10 citations · 2022
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