Pratik Kunapuli
Papers
7
Total Citations
379
H-Index
5
About
Pratik Kunapuli is a researcher working at the intersection of robotics, machine learning, and human movement science, with particular expertise in wearable robotic systems and autonomous aerial vehicles. His most influential contributions lie in the development of intelligent control systems for lower-limb exoskeletons, where he has pioneered neural network-based approaches to real-time gait phase estimation. His 2019 paper on this topic has accumulated 162 citations, with a closely related 2021 extension reaching 153 citations — together representing foundational work in enabling exoskeletons to deliver precise, context-aware assistance across diverse locomotion modes. Kunapuli has also advanced the field's understanding of how electromyography signals can inform environmental estimation, such as walking speed and terrain slope, further improving exoskeleton adaptability. More recently, he has explored personalized exoskeleton control for stroke patients through online adaptation frameworks, addressing a critical barrier to real-world clinical adoption. Expanding beyond wearable robotics, his work on Vision Transformer-based quadrotor obstacle avoidance demonstrates a broader commitment to applying cutting-edge deep learning architectures to complex real-time robotic challenges. Kunapuli's research consistently bridges theoretical machine learning innovation with meaningful practical impact in assistive technology and autonomous systems.
Research Focus
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Top Papers
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