Viral Rasik Galaiya
Papers
3
Total Citations
12
H-Index
1
About
Viral Rasik Galaiya is a robotics researcher advancing the frontier of tactile perception and dexterous manipulation. His work centers on integrating tactile sensing with reinforcement learning to enable robots to operate effectively under visual uncertainty—a critical challenge in real-world environments plagued by occlusion, lighting changes, and clutter. In his highly cited 2023 paper, Galaiya explored how tactile temporal features can be used for object pose estimation during robotic manipulation, demonstrating that touch alone can robustly infer an object’s orientation when vision fails. He further extended this work by developing compliant tactile sensing modules combined with reinforcement learning to improve grasp success under positional uncertainty (2024). Most recently, his 2025 study on extracting non-regular pegs using tactile sensing and learning from human demonstrations addresses a key bottleneck in assembly and disassembly tasks, where visual occlusion is inevitable. Collectively, his research has garnered over a dozen citations, establishing him as an emerging voice in tactile robotics. Galaiya’s contributions are particularly notable for bridging the gap between human demonstration and autonomous robotic skill acquisition, paving the way for more resilient and adaptable robotic systems in manufacturing and service applications.
Research Focus
Key Achievements
Top Papers
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