Events

Research Talk Robot Data is Not Enough Data
Export Event Save iCal Send to Google Calendar Send to Yahoo Calendar Send to Outlook Live Send to Microsoft Outlook Save iCal Preserve formatting in description (only supported in some calendar applications) Close Wednesday, February 11, 2026, 10:00am - 12:00pm

Speaker: Lerrel Pinto

Bio

Lerrel Pinto is the co-founder of Assured Robot Intelligence (ARI) and an Assistant Professor of Computer Science at NYU. His research aims to get robots to generalize and adapt in the messy world we live in. To this end his work focuses broadly on robot learning and decision making, with an emphasis on large-scale learning (both data and models), representation learning for sensory data, developing algorithms to model actions and behavior, reinforcement learning for adapting to new scenarios, and building open-sourced affordable robots. This work has received best paper awards at ICRA 2016, RSS 2023, and ICRA 2024. Lerrel has received the Samsung AI researcher of the year award, Sloan Fellowship, Packard Fellowship, CIFAR Fellowship, TR35 innovator under 35, IEEE RAS Early Career and NSF CAREER awards. Several of his works have been featured in popular media such as The Wall Street Journal, TechCrunch, MIT Tech Review, Wired, and BuzzFeed among others. His recent work can be found on www.lerrelpinto.com.

Location : Edward J. Bloustein School of Planning and Public Policy 33 Livingston Avenue New Brunswick, NJ 08901 Room 261

Committee: 

Event Type: Research Talk

Abstract: 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.

Organization: 

Contact  Professor Kostas Bekris