The Generalist Why Robots Still Struggle With Simple Tasks (And What Might Finally Change That) | Karol Hausman, Co-Founder & CEO of Physical Intelligence
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Mar 17, 2026 
Karol Hausman, co-founder and CEO of Physical Intelligence who previously researched robot learning at Google Brain and Stanford, talks about building a general AI brain for robots. He discusses combining LLM priors with motion models, why real-world data beats simulation for manipulation, the return of reinforcement learning, and the challenge of achieving near-perfect reliability for physical agents.
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One Lecture Rewrote His PhD And Career
- A visiting postdoc urged Karol to drop his PhD topic and switch to deep learning after a single lecture, which became a pivotal career turn.
- That lecture with Sergey Levine convinced him deep learning could unify robot learning approaches and he changed direction immediately.
Optimize For Fast Learning With Diverse Real Data
- Optimize for the rate of learning by collecting diverse, high-quality real-world data and closing the loop between models and deployments.
- Karol says once robots hit deployable threshold they create a data flywheel: deployed robots produce the most valuable real deployment data.
Why Simulation Struggles For Manipulation
- Simulation scales for locomotion because modeling the robot body suffices, but manipulation fails because you must simulate everything the robot interacts with.
- Modeling friction, object dynamics and diverse environments at scale is intractable, so real-world data is more scalable for manipulation.
