Armando Granado

Papers

Total Citations

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About

Armando Granado’s research sits at the intersection of augmented reality (AR) and robotics, where he pioneers intuitive human-robot interaction systems. His most impactful work, an AR framework for robotic tool-path teaching (24 citations), addresses a critical industrial bottleneck: the need for non-expert end-users to quickly reprogram robots in response to shifting production demands. By overlaying virtual toolpaths onto real-world environments, Granado’s system allows workers to “teach” robots through natural, visual demonstrations rather than complex code, dramatically reducing downtime in agile manufacturing. He extends this concept to mobile service robots with an AR spatial referencing system (7 citations), enabling domestic robots to dynamically update their environmental knowledge through human-guided AR markers—a key step toward truly autonomous home assistants. Granado’s contributions are notable for their practical, user-centered design: rather than requiring users to adapt to robotic interfaces, his work adapts robots to human spatial reasoning. This approach has been recognized for its potential to democratize robotics programming, making advanced automation accessible to small and medium enterprises. His research continues to shape how AR can bridge the gap between human intent and robotic action.