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About

Shreyansh Daftry is a robotics and artificial intelligence researcher whose work spans autonomous navigation, computer vision, and machine learning for robotic systems operating in extreme and unstructured environments. His research has made significant contributions to both aerial and ground robotics, with a particular focus on enabling intelligent autonomy in settings where conventional methods fail. Daftry gained prominent recognition through his involvement with TEAM CoSTAR in the DARPA Subterranean Challenge, where the NeBula autonomy framework he co-developed helped the team win Phase II of the competition — work that has collectively accumulated over 150 citations. His earlier research pioneered deliberative monocular flight for UAVs and transfer learning for micro aerial vehicle control, demonstrating how robots can adapt vision-based policies across varied environments — contributions that have each drawn tens of citations from the robotics community. His impact extends beyond Earth, with notable work on Mars and lunar robotics through NASA JPL's MAARS initiative and the LunarNav crater-based localization system, developed to support the Artemis program's ambitious long-range rover navigation requirements. He has also explored planetary cave mapping using heterogeneous robot teams. Across his career, Daftry's research consistently bridges cutting-edge machine learning with real-world deployment on some of the most challenging robotic platforms imaginable.