Colloquium Details
How to Close the 100,000 Year “Data Gap” in Robotics
Speaker: Ken Goldberg, UC Berkeley and Ambi Robotics
Location: 60 Fifth Avenue 150
Date: September 19, 2025, 11 a.m.
Host: Lerrel Pinto, Ludovic Righetti
Synopsis:
Large models based on internet-scale data can now pass the Turing Test for intelligence. In this sense, data has "solved" language and many analogously claim that data has solved speech recognition and computer vision. Will data also solve robotics and automation, allowing general-purpose humanoid robots to achieve human-level performance? Using commonly accepted metrics for converting word and image tokens into time, the amount of internet-scale data used to train contemporary large vision language models (VLMs) is on the order of 100,000 years. I’ll review 3 ways researchers are pursuing to close this gap, and a 4th approach, where data is collected as real robots operate in real commercial environments -- which requires bootstrapping with AI and "good old-fashioned engineering" to create robots with real return on investment that will be adopted by industry. Such robots can create a "data flywheel" to increase performance and enable new functionality, accelerating the timeline to achieve reliable, general-purpose robots.
Speaker Bio:
Ken Goldberg is William S. Floyd Distinguished Chair of Engineering at UC Berkeley and Chief Scientist of Ambi Robotics and Jacobi Robotics. Ken leads research in robotics and automation: grasping, manipulation, and learning for applications in warehouses, industry, homes, agriculture, and robot-assisted surgery. He is Professor of IEOR with appointments in EECS and Art Practice. Ken is Chair of the Berkeley AI Research (BAIR) Steering Committee (60 faculty) and is co-founder and Editor-in-Chief emeritus of the IEEE Transactions on Automation Science and Engineering (T-ASE). He has published ten US patents, over 400 refereed papers, and presented over 600 invited lectures to academic and corporate audiences.
Notes:
In-person attendance only available to those with active NYU ID cards.