Sergey Levine, co-founder of Physical Intelligence, discusses using multimodal LLMs to give robots common-sense reasoning in unusual physical situations.
Opinion
Levine: General Robotics Models May Ultimately Be Easier Than Narrow Ones
“And part of the thesis of this company is that we believe that doing it at the full level of generality might actually in the long run be easier than trying to special case very specific narrow application domains.”
Prediction Not checkable as stated
Levine: Multi-robot data will enable foundational physical models for rapid deployment
“So if we can draw on data from many sources, many applications, many robots, then we can have a model that has a physical understanding, and it'll be much, much easier to put new applications on top of that platform.”
Opinion
Levine: Future Robots Won't Just Be Humanoid 'Metal People'
“And I think, you know, we, sometimes we think that, like, robots are going to be, like, one thing. Like, it's just like, you know, there's people, and now we're going to make, like, metal people, and that'll be, like, robots. But I don't think that's how it's …”
Prediction Open · timeframe Mar 2031
Levine: Robotic foundation models will adapt across diverse physical form factors
“And I think that in the future we'll have A robotic foundation model, which can then be adapted to all sorts of applications, and they might really run the gamut from like, you know, like bulldozers or something, to humanoids, to robotic arms like this thing, …”
Insight
Levine: Chain-of-thought reasoning allows robots to handle edge cases
“So the way you get common sense is by essentially using chain of thought. So the robot enters a scene and instead of directly starting to move, it thinks about what it was asked to do. So if it was told to clean up the kitchen, looks at the scene and says, lik…”
Insight
Levine: Effective AI Learning Methods Compensate for Deficient Hardware Sensing
“A good learning method can actually like compensate for deficient sensing fairly well.”