Unmesh Patil

Visvesvaraya National Institute of Technology

Papers

Total Citations

H-Index

About

Unmesh Patil is a robotics and artificial intelligence researcher whose work sits at the compelling intersection of deep learning, computer vision, and autonomous mobile systems. His most significant contributions focus on enabling robots to perceive and navigate complex real-world environments, with a particular emphasis on stair detection and traversal — a critical capability for robots deployed in urban search and rescue, surveillance, and military operations. His 2019 paper on deep learning-based stair detection and statistical image filtering has garnered 38 citations, establishing him as a notable voice in autonomous navigation research. Complementing this, his work on semantic stair segmentation and behavioral cloning demonstrates a sophisticated understanding of how robots can learn from human demonstrations to master challenging locomotion tasks. More recently, Patil extended his expertise to the design and development of autonomous delivery robots, reflecting the growing real-world demand for reliable, sensor-rich robotic systems across industries ranging from healthcare to logistics. Collectively, his research addresses fundamental challenges in robot perception and decision-making, making meaningful contributions to the development of safer, more capable autonomous systems suited for dynamic, unstructured environments.