Hosted as part of the Machine Learning and AI Seminar Series in partnership with the DSI Foundations of Data Science Center and the Department of Statistics, Arts and Sciences, Columbia Engineering

Speaker

Lerrel Pinto, Assistant Professor of Computer Science, NYU Courant 

Event Details

Friday, February 6, 2026 (11:00 AM – 12:00 PM ET)

Location: Hamilton Hall, Room 702

REGISTRATION DEADLINE: The Columbia Morningside campus is open to the Columbia community. If you do not have an active CUID, the deadline to register is at 12:00 PM the day before the event.

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Talk Information

The past decade of robot learning has been fueled by piles of human-teleoperated robot data. But this strategy is hitting a wall. Unlike computer vision and natural language processing, fields supercharged by mountains of passive, internet-scale human-labeled data, robotics faces a harsher reality. Robot data is expensive. It is slow. It is narrow. And most critically, we don’t even know which demonstrations or labels truly matter for embodied intelligence. Chasing more of the same is a dead end. 

In this talk, I will argue that robot data alone will never deliver the leap we need. We must demand more. Robots should learn directly from humans. They should feel the world through touch, rather than staring at pixels alone. And they must go beyond purely reactive modes and instead reason, plan, and act with foresight. If we are serious about building intelligent machines, we must move beyond the fixation on “just more data” and instead embrace the hard, messy, human-centered problems that will define the next era of robotics.