Papers
2
Total Citations
6
H-Index
2
About
Y V S Harish is a robotics researcher whose work bridges classical control theory with modern deep learning approaches. His key research areas include autonomous navigation, visual servoing, and robot learning. Harish’s major contributions are exemplified by two notable projects: ROBOG, an autonomous robot guide that uses Artificial Neural Networks to learn and navigate known terrains, demonstrating a simple yet effective learning strategy for decision-making. This work, published in 2014, has garnered 4 citations and showcases his early interest in integrating neural networks with robotics. More recently, his 2021 paper “DeepMPCVS: Deep Model Predictive Control for Visual Servoing” tackles the challenge of precise visual alignment in unseen environments—a persistent hurdle in vision-based robot control. By combining deep learning with model predictive control, Harish addresses the limitations of classical visual servoing, offering a more robust solution for real-world applications. Though his citation counts are modest, his work reflects a thoughtful progression from foundational autonomous systems to advanced, learning-driven control methods. Harish’s research is particularly valuable for students and researchers interested in the intersection of neural networks, control theory, and practical robotics.
Research Focus
Key Achievements
Top Papers
- 1ROBOG: Robo guide with simple learning strategy4 citations · 2014
- 2DeepMPCVS: Deep Model Predictive Control for Visual Servoing2 citations · 2021