Yash Savle

Papers

Total Citations

147

H-Index

About

Yash Savle’s research lies at the intersection of robotics, computer vision, and human-robot interaction, with a focus on socially aware navigation in crowded environments. His most impactful work addresses the urgent need for automated social distancing monitoring during the COVID-19 pandemic. In two highly cited papers (2020 and 2021, with 76 and 55 citations respectively), Savle developed novel computer vision methods to automatically detect pairs of individuals violating the recommended 6-foot (2-meter) spacing in crowded indoor and outdoor scenarios. These systems, designed for integration into surveillance robots, provided a scalable, real-time tool for public health compliance. Building on this foundation, Savle’s work on “CoMet” (2021, 16 citations) introduced a groundbreaking approach to robot navigation by modeling group cohesion. Drawing from social psychology, CoMet computes a cohesion metric from visual pedestrian features, enabling robots to navigate crowded spaces more naturally and respectfully by understanding and anticipating the dynamics of social groups. This contribution is particularly significant for the development of socially compliant robots that can operate seamlessly in human-centric environments, from hospitals to public squares. Savle’s research demonstrates a clear trajectory from solving an immediate global health challenge to advancing the fundamental principles of human-aware robotics.