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About

Anchit Gupta is a robotics and machine learning researcher whose work sits at the intersection of robot learning, imitation learning, and scalable data collection for embodied AI. He has made significant contributions to the infrastructure and methodology that enable robots to learn complex manipulation skills, with a particular focus on democratizing access to large-scale training resources. Among his most notable achievements is the development of RoboTurk, a crowdsourcing platform that leverages human demonstrations to train robots through imitation learning — a system he helped scale to hundreds of hours of annotated robotic manipulation data. His work on SURREAL, an open-source distributed reinforcement learning framework, has similarly shaped how researchers benchmark and train robotic agents, accumulating over 118 citations. Gupta also contributed to the landmark Open X-Embodiment project (119 citations), a large-scale collaboration producing diverse robotic datasets and the RT-X model family, advancing the vision of generalist robot policies akin to foundation models in NLP and computer vision. Collectively, his research has helped address a fundamental bottleneck in robotics: the scarcity of rich, diverse training data. His combined citation impact reflects meaningful influence on how the field approaches scalable, generalizable robot learning.