Sergey Levine

@sergey-levine · 24 positions · On ppll ↗

Professor of computer science at UC Berkeley, and a co-founder of the robotic foundation model company Physical Intelligence.

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What Sergey Levine says most about

robotics 18

Robots will learn from deployment more easily than chatbots do, because a physical mistake is obvious the moment it happens.
Language models supply robots with the one thing self-driving never had: common sense about what is likely to happen next.

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LLMs 3

Language models supply robots with the one thing self-driving never had: common sense about what is likely to happen next.
Emergent abilities come from generalisation turning compositional, not merely from a training set containing a lot of stuff.

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computer vision 2

Having a job to do is what makes perception tractable, which is why an embodied model can learn from vision where a video model cannot.
Video never became intelligence the way text did because text arrives already abstracted into the bits humans care about.

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reinforcement learning 2

The goal was never good simulation; it was answering counterfactuals, and a value function does that job as well as a simulator does.
Reinforcement learning only works once a model already knows something, which is why robots must be pre-trained by imitation first.

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self-driving cars 2

Language models supply robots with the one thing self-driving never had: common sense about what is likely to happen next.
Robotics is not easier than self-driving; it is simply being attempted in a year when perception actually generalises.

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world models 2

Synthetic experience can only rehearse what a system already knows; information about the world has to be injected from outside.
The goal was never good simulation; it was answering counterfactuals, and a value function does that job as well as a simulator does.

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