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About

Sanjiban Choudhury is a robotics researcher whose work spans autonomous navigation, imitation learning, and human-robot interaction, with a particular focus on making robots safer, smarter, and more learnable in real-world environments. His research addresses some of the most pressing challenges in mobile robotics: how robots can plan efficiently under uncertainty, learn from human feedback, and operate safely in partially known environments. Choudhury's contributions to motion planning are notable, including his development of generalized lazy search algorithms that intelligently interleave search and edge evaluation to reduce computational overhead — work that has attracted sustained attention in the planning community. His investigations into imitation learning are equally significant; papers like "Learning from Interventions" (42 citations) and "Expert Intervention Learning" examine how robots can leverage both explicit and implicit human feedback to scale learning effectively. His theoretical treatment of covariate shift further sharpens understanding of when and why interactive imitation learning outperforms offline approaches. Beyond algorithms, Choudhury has made accessible contributions to the broader robotics community through MuSHR (45 citations), an open-source robotic racecar platform enabling multi-agent research at low cost. With over 230 cumulative citations across his most influential works, his research consistently bridges rigorous theory with practical deployability.