Dhruv Batra: The Browser Is a Robotics Problem - From Embodied AI at Meta to Web Agents at Yutori

Dhruv Batra spent years leading Embodied AI at Meta,  training virtual robots to navigate photorealistic 3D scans of real buildings with pure reinforcement learning. Then he left to co-found Yutori and build agents for a very different environment: the web browser.In this episode, Dhruv explains why he sees these as the same problem. Web agents, in his framing, are robots that act in a browser (pixels in, actions out), and the web turns out to be just as messy an environment as the physical world.Along the way, we cover his definition of intelligence as "navigation in idea space," why robotics is lagging LLMs, the sim-to-real gap and why you can't fake friction coefficients, the teleoperation counterexample to the "it's a sensor problem" argument, and his provocative claim that under the current paradigm, we solved machine learning and didn't even realize it. He also makes the case for why the scaling hypothesis isn't falsifiable, why JEPA-style arguments deserve to be grappled with, how Yutori trains its Navigator models with RL on live websites, and what happens to the ad-supported web when agents, not eyeballs, do the browsing.Timeline00:01 — Intro00:54 — What embodied AI actually means06:47 — Intelligence as navigation in idea space13:26 — Habitat: training robots with pure RL, no maps20:04 — Why robotics is behind LLMs28:24 — Sim-to-real: what you can and can't fake33:34 — "We solved ML and nobody noticed"37:12 — Leaving Meta, founding Yutori43:21 — Web agents: screenshots in, actions out48:15 — Why the web won't rebuild itself for agents53:32 — Training Navigator: RL on live websites1:01:04 — Who pays for the web when agents browse?1:09:17 — What Yutori means, closing thoughtsMusic"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.AboutThe Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.