Samarth Kalluraya

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About

Samarth Kalluraya is a robotics researcher whose work sits at the intersection of formal methods, multi-robot coordination, and semantic perception. His primary research focuses on developing resilient, perception-driven planning algorithms that enable teams of robots to execute complex missions in uncertain and dynamic environments. Kalluraya’s major contributions include pioneering a framework for multi-robot planning in environments with partially unknown semantics, where static landmarks have uncertain positions and classes—a problem formalized in his highly cited 2022 paper (41 citations). He further extended this to handle mobile, stochastic semantic targets in his 2023 work (19 citations), addressing the critical challenge of planning amidst moving, uncertain objects. Notably, his 2023 paper on resilient temporal logic planning (8 citations) tackles the practical issue of robot failures, designing paths that remain reactive to lost capabilities. With over 68 total citations, Kalluraya’s work is shaping how autonomous teams can reliably perform high-level missions specified in formal languages like Linear Temporal Logic (LTL), making him a key voice in advancing robust, semantics-aware robotics for real-world deployment.