Hasitha Wellaboda
Papers
1
Total Citations
3
H-Index
1
About
Hasitha Wellaboda is a researcher at the intersection of robotics, augmented reality (AR), and artificial intelligence, with a focus on human-robot collaboration. Their most cited work, "Deep Learning of Augmented Reality based Human Interactions for Automating a Robot Team" (2020, 3 citations), introduces a novel system where a team of robots learns complex tasks through manual manipulation in an AR environment. This contribution is significant because it moves beyond simple teleoperation, enabling robots to autonomously internalize and replicate human-taught behaviors. By integrating deep learning with AR-based human interactions, Wellaboda’s research paves the way for more intuitive and scalable multi-robot systems. Their work addresses a critical challenge in robotics: how to efficiently transfer human expertise to autonomous agents without explicit programming. While still early in their career, this foundational paper demonstrates a clear vision for the future of human-robot teamwork, where augmented reality serves as a bridge for seamless skill transfer. Wellaboda’s contributions are particularly relevant for researchers exploring interactive machine learning, collaborative robotics, and AR interfaces in automation.
Research Focus
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Top Papers
- 1