Viral Galayia
Papers
1
Total Citations
3
H-Index
1
About
Viral Galayia is a rising robotics researcher whose work lies at the intersection of tactile sensing, multimodal perception, and autonomous manipulation in unstructured environments. His research addresses a critical gap in robotics: the need for systems to adapt to real-world uncertainty beyond what vision alone can provide. Galayia’s most cited work, “A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modules” (2025, 3 citations), introduces a novel dataset that integrates tactile feedback from bio-inspired sensor modules with visual data, enabling robots to perform precision tasks like peg extraction under occlusion and variable conditions. This contribution is foundational for advancing robotic dexterity in manufacturing and assembly, where traditional camera-based systems often fail. By emphasizing tactile awareness, Galayia’s work pushes the boundaries of embodied intelligence, offering a pathway to more resilient and adaptive robotic systems. His research is particularly notable for its practical focus on sensor fusion, bridging the gap between simulation and real-world deployment. As a young investigator, Galayia’s early impact signals a promising trajectory in the field of robotic manipulation and sensorimotor learning.
Research Focus
Key Achievements
Top Papers
- 1