Vikram Shree

Cornell University, Indian Institute of Technology Kanpur

Papers

Total Citations

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About

Vikram Shree is a robotics researcher whose work spans multi-robot systems, autonomous navigation, and the application of artificial intelligence to safety-critical environments. His most notable contributions lie at the intersection of natural language processing and robot planning, particularly in the context of search and rescue (SaR) missions. In his most-cited work, Shree pioneered an approach that uniquely leverages natural language descriptions from human commanders alongside image data to enable risk-aware, efficient multi-robot SaR planning — a contribution that has garnered 15 citations and represents a significant step toward more intuitive human-robot collaboration. Extending this vision, his 2022 research explored how robots can learn to assess environmental danger by drawing on cinematic depictions of hazardous scenarios, addressing the fundamental challenge of training autonomous systems for situations that are difficult to safely replicate in the real world. Earlier in his career, Shree contributed to the foundational robotics problem of Simultaneous Localization and Mapping (SLAM), proposing a framework that separates orientation and position estimation using relative feature measurements. Together, his body of work reflects a consistent commitment to making autonomous robots safer, smarter, and more deployable in real-world emergencies.