Akkamahadevi Hanni

KLE Technological University, Arizona State University

Papers

Total Citations

H-Index

About

Akkamahadevi Hanni’s research lies at the intersection of **computer vision, human-robot interaction, and explainable AI**, with a focus on enabling robots to understand and collaborate seamlessly with humans in indoor environments. Her early work introduced a **deep learning framework for scene-based indoor location recognition** (2017, 9 citations), advancing how robots perceive and navigate complex interior spaces—a critical step for assistive robotics and autonomous systems. More recently, Hanni has pioneered **active explicable planning** for human-robot teaming (2021, 3 and 1 citations), where she addresses a fundamental challenge: ensuring robots not only perform tasks but also generate plans that are transparent and aligned with human expectations. Her research emphasizes that effective collaboration requires robots to proactively communicate their reasoning, reducing misunderstandings in shared tasks. By bridging perception and socially-aware planning, Hanni’s work contributes to building trustworthy, intuitive robotic teammates for real-world applications. Her growing citation record reflects the emerging importance of explainable autonomy, positioning her as a thoughtful voice in the future of human-centered robotics.