Abhinav Gupta

Abhinav Gupta is a Professor in the Robotics Institute at Carnegie Mellon University, where he conducts research in computer vision, machine learning, and robotics. His research focuses on developing scalable learning methods, including self‑supervised and lifelong learning systems for visual and robotic understanding.

Education

Gupta earned a Bachelor’s degree in Computer Science and Engineering from the Indian Institute of Technology, Kanpur and a PhD in Computer Science from the University of Maryland. Prior to joining Carnegie Mellon University, he was a postdoctoral researcher working with faculty in the CMU Robotics Institute.

Career

Gupta joined the Robotics Institute at Carnegie Mellon University as faculty in 2011, after serving as a postdoctoral researcher.His work addresses fundamental problems in scene representation, reasoning, and learning from visual and sensory data, with an emphasis on self‑supervised methods that reduce reliance on labeled data.

His research contributions include scalable methods for learning general-purpose visual representations, learning from human videos for robotics applications, and approaches that allow robots to acquire skills through experience-driven and multimodal learning.

Gupta was one of the first to demonstrate that robots can learn visual representations directly from interaction, without human-labeled data, at scale.His research group worked on The Never Ending Image Learner (NEIL) model of computing.

He led some of the earliest efforts to collect large, real-world robotic manipulation datasets, not simulation-only benchmarks.

Gupta was appointed as the director of the AI Lab opened by Facebook in Pittsburgh in 2018.As a senior leader at Meta AI Robotics, he operationalized these IDeaS in production-scale research systems.

He co-founded Skild AI with Deepak Pathak at Carnegie Mellon University.

Awards and honors

Gupta has received numerous academic awards, including the Sloan Research Fellowship and the J.K. Aggarwal Prize for self‑supervised learning from the International Association for Pattern Recognition, the Office of Naval Research Young Investigator Award.

References

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