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**Chelsea Finn** (0:00)
I think the biggest risk is that everyone fails, that robotics as a whole fails, because robotics is so hard. There's so many pieces you have to put in place for anything to work. I think that when watching a robot demo, if there are details about how it was done, it's really important to read those and actually understand how that was developed. Was it developed in a way that is going to, like in the long run, stand the test of time and be scalable? Pi models are already running in production. Gives me optimism that we are at the point where this technology is mature enough to be useful.

**Reid Hoffman** (0:30)
Most of the AI revolution has happened behind glass, in search boxes, chat windows, image generators. The world we actually live in is made of objects that slip, doors that stick, and rooms no model has seen.

**Aria Finger** (0:43)
That's why robotics remains one of the deepest tests of intelligence. It's one thing to describe a warehouse, it's another to walk into an unfamiliar building, read a new label, pack a box it's never seen, and recover when something goes wrong.
Without being hand programmed for any of it.

**Reid Hoffman** (1:01)
Chelsea Finn has worked on that problem from both sides of the frontier. At Stanford, her research on meta-learning helped to find one of the central questions in modern AI.
Her MAML paper has been cited more than tens of thousands of times.

**Aria Finger** (1:16)
At Physical Intelligence, the company she co-founded in 2024, that question has gotten very literal. Can a single model generalize broadly enough that robots don't need to be reprogrammed for every task, every warehouse or every failure mode?

**Reid Hoffman** (1:31)
The factual version of that story includes months of 0% success rates on laundry folding before a single architectural insight unlock the capability. Pai has since demonstrated a sequence of increasingly capable generalist policies.

**Aria Finger** (1:48)
Today's conversation is about what it will take for AI to leave the screen and what that transition reveals about intelligence itself.

**Reid Hoffman** (1:56)
Chelsea Finn, welcome to Possible. Welcome.
You said that folding laundry is the most impressive thing you've seen a robot do. I want to start not with the result, but with the months of 0% success rates before it happened. What were those failures actually teaching you? Yeah.

**Chelsea Finn** (2:17)
I think the first thing I'll say is that robotics is really, really hard and it's really easy to underestimate how hard it is because we are so good at manipulating all sorts of things around us. With our hands, it comes second nature. We don't even think about how we go about flattening a shirt and folding it when we're folding laundry. So it's really easy to take for granted the fact that it's not too hard for us to manipulate things, but actually for a robot, you need to translate all of the sensor readings, all of the different RGB pixel values into a vector of numbers, a large vector of numbers for all the different joints over time of the robot to do.
And the thing that I think specifically I found about laundry is that there are so many different ways for even just a single shirt or a single set of shirts to be crumpled and configured and dealing with that variability is very challenging because the robot needs to understand how to translate all of these different configurations of a shirt into actions that will actually make progress on the task. And so one of the things we had found previously is we were able to train robots to do tasks in narrow situations and once it broadened out to be even for a single shirt but broadened to be a much wider range of configurations, the problem gets a lot harder.
And we started with something simple and we had some results where if you start with the shirt flat, it's able to fold it. And usually in research, it's good to start with something that works, then make it incrementally harder. In this case, it was just a scenario where we went from a flat shirt to a crumpled shirt made it way, way harder. And that's where you do a little bit of banging your head against the wall for a few months before you actually start to see signs of life.

**Aria Finger** (4:01)
I mean, honestly, watching your videos, I was like, no, what if the shirt's inside out? They're never going to be able to do it. And it's like, what are these things? It's so funny because you're like, wait, so a car can be self-driving and drive down the highway at 60 miles an hour, but the robot can't fold the shirt. Like it's just such an interesting disconnect. And so you're taking on the physical world and like you said, like the physical world is so hard.