Behavioral cloning mystery I wrote a new blog post about "mysteries" in behavioral cloning that appear with real-world robot data (e.g., overfitting is "good"). I also tried to demystify them and shared my thoughts!

Sergey Levine (@svlevine) is one of the world's top robotics researchers and co-founder of Physical Intelligence. We talked about where humanoid robotics is today, thoughts on the Chinese robotics ecosystem, and his predictions for future timelines. In this episode: • Current state of robotics and surprising capabilities so far • Chinese robotics compared to US ecosystem • If OpenAI and Anthropic got into robotics • His top robotics research paper recommendation • Predictions for when humanoid robotics will land Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 Intro 00:37 Where are we today 04:20 Most surprising capabilities so far 07:03 The most inspiring real world robotics 08:36 If OpenAI or Anthropic got into robotics 10:22 Chinese robotics 13:15 Will one lab breakout from the rest 16:59 Thoughts on a concrete roadmap 21:03 Generalization and demonstrating it 26:04 Types of data and which is best for robotics 34:34 Why humanoid robotics differs from Waymo 37:10 If humanoid robotics failed here is why 39:55 Are there hot take modeling architectures in robotics 42:05 Thoughts on AI safety in robotics 46:44 Top robotics research paper recommendation 49:35 Why is Boston Dynamics less top of mind 53:47 Advice for his younger self 56:42 Outro

Robots can already fold laundry, make espresso, clean kitchens, and assemble things. The harder problem is getting them to do those tasks reliably, for long periods of time, without a human babysitting them. At Startup School 2026, @physical_int cofounder @chelseabfinn explains what it takes to build general-purpose robots that work in the real world. She shares how reinforcement learning pushed robot throughput up 2x, how their systems can run autonomously for hours, and why she believes robotics is entering its GPT era: moving from specialized models toward general-purpose systems that can work across tasks, robots, and environments. 00:00 — The State of Physical Intelligence 01:23 — What It Takes to Make Robots Useful 05:11 — The Reliability Problem 07:43 — Reinforcement Learning for Robotics 09:35 — Learning From Failures 12:43 — Training Robots to Improve Themselves 14:21 — Can a Robot Work for 13 Hours Straight? 17:36 — Why Robots Need Memory 21:22 — Building a General-Purpose Robot 25:02 — From Fine-Tuning to Out-of-the-Box Models 27:35 — Training on All the Data 30:20 — One Model That Beats the Specialists 31:21 — Compositional Generalization 37:49 — The GPT Era of Robotics 39:49 — Q&A

Action chunking is a critical component in virtually all modern approaches to imitation learning for robotics. But why is it so critical, and do we really need action chunking? Check out our latest work to find out! (1/n)

This is a robot failing to grasp a ball. Almost every robot lab produces clips like this daily… and almost all of them get thrown away. This is the most abundant but underused resource in robot learning. We’re collecting all of it now as ✨OopsieData✨, please join us!

This morning at ICML 2026, I'll be speaking at the Workshop on Decision-Making from Offline Datasets. Come to ASEM Ballroom 203 at 8:15 am KST! I'll talk about foundations of data-driven decision making, from design via model-based optimization to offline RL