Papers
7
Total Citations
29
H-Index
3
About
Madan M. Rayguru is a rising researcher at the intersection of human-robot interaction (HRI), assistive robotics, and adaptive machine learning. His work focuses on making robots more intuitive and responsive to human needs, particularly in healthcare and rehabilitation settings. Rayguru’s most cited paper, “Multi-Joint Adaptive Motion Imitation in Robot-Assisted Physiotherapy with Dynamic Time Warping and Recurrent Neural Networks” (2024, 12 citations), introduces a novel framework for enabling robots to mimic complex human joint movements, a breakthrough for at-home physiotherapy. He has also developed an adaptive user interface using parallel neural networks for teleoperation and pioneered personalized speech emotion recognition with vision transformers, enhancing affective HRI. His neural human intent estimator for the Adaptive Robotic Nursing Assistant (ARNA) addresses critical safety and predictability in service robotics. Beyond healthcare, Rayguru has contributed to intelligent control algorithms, such as the IQ-CRL algorithm, which improves Q-learning with artificial neural networks for mobile robots. With a growing citation record and a portfolio spanning rehabilitation, nursing assistance, and autonomous cleaning robots, Rayguru is shaping the future of adaptive, human-centered robotics.
Research Focus
Key Achievements
Top Papers
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- 4Neural Human Intent Estimator for an Adaptive Robotic Nursing Assistant3 citations · 2024
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