Akshay Kulkarni

Visvesvaraya National Institute of Technology

Papers

Total Citations

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About

Akshay Kulkarni is a researcher working at the intersection of deep learning, computer vision, and autonomous robotics. His work focuses on enabling machines to perceive and navigate complex real-world environments, with particular emphasis on mobile robot autonomy and generative video models. Kulkarni's most recognized contribution is his 2019 paper on deep learning-based stair detection for autonomous robots, which introduced a novel approach combining neural networks with statistical image filtering to enable robots to traverse staircases — a critical capability for urban search-and-rescue and surveillance applications. This work has accumulated 38 citations, reflecting its practical significance within the robotics community. Beyond navigation, Kulkarni has explored the frontier of video generation, authoring a 2020 review of generative approaches to video synthesis that surveys methods for predicting trajectories and modeling object motion, supporting advances in autonomous systems. His 2021 work on autonomous delivery robots further demonstrates his commitment to translating research into tangible real-world applications. Collectively, his research paints a portrait of a scientist dedicated to building smarter, more capable autonomous systems — making his profile highly relevant for students pursuing careers in robotics and applied deep learning.