Franklin Tigre
Papers
2
Total Citations
13
H-Index
2
About
Franklin Tigre is a researcher at the forefront of human-robot interaction, specializing in the application of convolutional neural networks (CNNs) to enhance the gestural communication of social robots. His work focuses on bridging the gap between human non-verbal cues and robotic responsiveness, enabling more intuitive and lifelike interactions. Tigre’s major contributions lie in developing CNN-based frameworks that allow humanoid social robots to interpret and replicate human gestures with greater accuracy and fluidity. His two most-cited papers, both published in 2019, collectively garnering 13 citations, lay the groundwork for this approach: one explores the broader gesticulation control of a social robot, while the other refines the method for a humanoid aspect. These studies demonstrate how deep learning can transform robotic behavior, making it more adaptive and socially aware. Tigre’s work is particularly notable for its practical implications in assistive robotics and interactive systems, where natural movement is key to user acceptance. By integrating computer vision with robotics, he is helping to shape a future where machines can engage with humans on a more expressive, human-like level.
Research Focus
Key Achievements
Top Papers
- 1
- 2