Srikanth Peetha

Papers

Total Citations

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About

Srikanth Peetha’s research lies at the intersection of human-machine interaction and adaptive robotics, with a core focus on making teleoperation systems more intuitive and efficient. His major contribution is the development of a genetic algorithm-based adaptive interface that dynamically optimizes user controls during robot teleoperation—a system that learns from operator behavior to reduce cognitive load and improve task performance. This work, detailed in his most-cited paper “Adaptive Interface for Robot Teleoperation using a Genetic Algorithm” (2018, 6 citations), addresses the long-standing challenge of designing interfaces for complex, multi-degree-of-freedom robots. To validate his approach, Peetha also designed and implemented a dedicated experimental setup, described in “Experimental setup for evaluating an adaptive user interface for teleoperation control” (2017, 3 citations), which provides a reproducible framework for studying adaptive HMI in real-world conditions. Though his citation counts are modest, his work represents a foundational step toward user-centered, self-improving control systems—a critical need as robots move from factories into homes and hospitals. Peetha’s research offers a promising path for students and engineers seeking to build interfaces that adapt to humans, rather than forcing humans to adapt to machines.