Sanket Lokhande
Papers
1
Total Citations
2
H-Index
1
About
Sanket Lokhande is a researcher at the forefront of explainable artificial intelligence (XAI) and autonomous robotics, with a particular focus on bridging the gap between high-performance deep learning and interpretable decision-making. His work addresses a critical challenge in robotics: ensuring that autonomous systems can reliably recognize and respond to situations even when trained on partial or unlabeled data. Lokhande’s most-cited paper, "A novel explainable AI-based situation recognition for autonomous robots with partial unlabeled data" (2023), introduces innovative techniques that allow neural networks to maintain transparency while operating under real-world data constraints—a vital contribution for safety-critical applications like unmanned vehicles. Though early in his career, his research has already garnered attention, with citations highlighting its potential to make autonomous systems both more trustworthy and robust. By tackling the inherent opacity of deep learning models, Lokhande is helping to pave the way for next-generation robots that can explain their reasoning, a key step toward broader adoption in industries ranging from logistics to defense. His work stands at the intersection of machine learning, robotics, and human-centered AI, promising safer and more accountable autonomous technologies.
Research Focus
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Top Papers
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