John Doherty
Papers
1
Total Citations
2
H-Index
1
About
John Doherty is a leading researcher in multimodal perception and robotic manipulation, with a focus on visuo-tactile object recognition. His most-cited work, "A Novel Visuo-Tactile Object Recognition Pipeline using Transformers with Feature Level Fusion" (2024), addresses the fundamental challenge of integrating visual and tactile data—which have inherently different statistical properties—for robust robotic interaction. This pipeline leverages transformer architectures to achieve feature-level fusion, enabling robots to more effectively perceive and interact with their environment. While early in its citation trajectory (2 citations to date), this work represents a significant step toward bridging the gap between vision and touch in autonomous systems. Doherty’s contributions are particularly impactful in the field of robotics, where reliable object recognition is critical for tasks ranging from industrial automation to assistive technologies. His research has the potential to enhance how robots understand and manipulate objects in unstructured, human-centric environments, paving the way for more intuitive and capable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1