Sergey Levine: Current State of Humanoid Robotics, China & Future Predictions

Sergey Levine 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.

• My ergonomic keyboard project I mentioned, you can follow along here: read.compose.llc
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Podcast links:

• YouTube: youtu.be/9OSbaPjv0Rc
• Apple: podcasts.apple.com/us/podcast/the-peterman-pod…
• Transcript: developing.dev/p/sergey-levine-current-state-o…

Thank you to this episode's sponsor 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 workos.com

Timestamps:

(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

Where to find Sergey:

• Google Scholar: scholar.google.com/citations?user=8R35rCwAAAAJ…
• Website: people.eecs.berkeley.edu/~svlevine
• Wikipedia: en.wikipedia.org/wiki/Sergey_Levine
• X/Twitter: x.com/svlevine?lang=en
• LinkedIn: linkedin.com/in/sergey-levine-5a31a24

Where to find Ryan:

• Newsletter: developing.dev
• X/Twitter: x.com/ryanlpeterman
• LinkedIn: linkedin.com/in/ryanlpeterman
• Threads: threads.com/@ryanlpeterman
• Instagram: instagram.com/ryanlpeterman
• TikTok: tiktok.com/@ryanlpeterman

Referenced in this episode:

• Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (ALOHA / ACT paper): arxiv.org/abs/2304.13705
• Emergence of Human to Robot Transfer in Vision-Language-Action Models: arxiv.org/abs/2512.22414
• Summary of human to robot paper: pi.website/research/human_to_robot