Ryan Punamiya

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About

Ryan Punamiya is an emerging robotics researcher whose work centers on imitation learning and robot manipulation, with a particular focus on scaling data collection for autonomous systems. His most notable contribution is **EgoMimic**, a full-stack framework that addresses one of the field's most pressing bottlenecks: acquiring sufficient, diverse demonstration data for training robotic policies. Rather than relying solely on costly teleoperation, EgoMimic leverages egocentric human videos paired with 3D hand tracking, enabling robots to learn manipulation skills from everyday human activity. This innovative approach bridges the gap between human embodiment and robot learning, dramatically expanding the pool of usable training data. The work has garnered over a dozen citations across its 2024 and 2025 iterations, reflecting strong early interest from the robotics and machine learning communities. By making imitation learning more scalable and accessible, Punamiya's research has meaningful implications for the broader deployment of capable robotic systems. His contributions signal a promising trajectory for researchers interested in data-efficient robot learning, human-robot knowledge transfer, and the intersection of computer vision with embodied AI.