Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Sergey Levine
[00:00:00] S0: Do you have a favorite robot that's not part of what Physical Intelligence is doing, and if so, why? Could be anything. Could be a factory robot, could be an Optimus or Boston Dynamics. [00:00:00] S1: I do really like the Boston Dynamics robot, especially the new version of the Atlas. Because it is in some ways very human-like and in some ways very not human-like. They made some interesting decisions about how they want more range of motion on the joints, so it can do some pretty cool things. It's also a very agile robot, which is really cool. It makes for those awesome demos, so I'm a big fan of that. I'm generally a big fan of everything that Boston Dynamics has done. [00:00:00] S0: Should or could anything be read into the fact that Boston Dynamics has been doing very cool demos for a very long time and don't actually do anything useful for customers? [00:00:00] S1: I think it's also a fair question for lots of robotics companies, to be fair. There is a lot of value in demos that serve to illustrate challenges on the road to something useful and productive. Obviously, you can also do a demo without being on the road to something useful and productive. There is value in demos. I think that demos that are used correctly in service to a mission can provide people with an illustration of what to expect, and they also provide a challenge. You just have to be honest in setting up that challenge.
· Sergey Levine, Patrick O'Shaughnessy
[00:00:00] S1: I think it's also a fair question for lots of robotics companies, to be fair. There is a lot of value in demos that serve to illustrate challenges on the road to something useful and productive. Obviously, you can also do a demo without being on the road to something useful and productive. There is value in demos. I think that demos that are used correctly in service to a mission can provide people with an illustration of what to expect, and they also provide a challenge. You just have to be honest in setting up that challenge. [00:00:00] S0: How much do you think about the business endpoints to this point? Roomba is the best-selling robot of all time in the consumer category, which is surprising. And of course, we might be on the edge of some sort of Cambrian explosion. But how much of your cycles do you spend thinking about, this is the shape of a product that might result from this, that maybe is the way we bootstrap our way to all this data?
· Patrick O'Shaughnessy, Sergey Levine
[00:00:00] S0: How much do you think about the business endpoints to this point? Roomba is the best-selling robot of all time in the consumer category, which is surprising. And of course, we might be on the edge of some sort of Cambrian explosion. But how much of your cycles do you spend thinking about, this is the shape of a product that might result from this, that maybe is the way we bootstrap our way to all this data? [00:00:00] S1: It's just something that's very hard to reduce to a very concrete answer right now. It's not too bad to think about a space of possibilities. A lot of what we're doing when we develop our models, when we experiment with different tasks, when we do demos like the Robot Olympics – underneath we're prototyping, what does it look like when we try to do something real with this, to different degrees of real, and what goes wrong? It is something we think about a lot. It's not something that I have even close to a concrete answer to, but there's a space of possibilities. And a lot of what we actually are planning to do in 2026 is also experiment with different things in that space. [00:00:00] S0: When you study the history of general purpose technologies – certainly this would be a major one if it comes to fruition – you often find this constellation of things happening around that thing that enable it. LLMs are a direct complement to what you're doing. Are there any other surprising technology areas or trends that help you do what you do but are different?
· Patrick O'Shaughnessy, Sergey Levine
[00:00:00] S0: When you study the history of general purpose technologies – certainly this would be a major one if it comes to fruition – you often find this constellation of things happening around that thing that enable it. LLMs are a direct complement to what you're doing. Are there any other surprising technology areas or trends that help you do what you do but are different? [00:00:00] S1: Robotics hardware has become dramatically more affordable over the last few years. When I started working in robotics about a decade ago, I worked with a robot called a PR2, which I believe had a cost of about $400,000. When I started my lab at UC Berkeley, I used a robot that was in the ballpark of $30,000. Now, each arm on this thing is maybe a tenth of that. We think that can be even less. That's not due to any one single technology. It involves both hardware and software. So the kind of low-cost arms that we have here, they wouldn't be useful in an industrial setting because traditional control methods that rely on a great deal of precision wouldn't be able to use them. And I think that does make it a lot more practical to think about general purpose robotics today. [00:00:00] S0: For people that would want to be fairly technical about following major milestones that are happening in this field, where does that information show up? What do you read to stay informed about what's going on, or watch?
· Patrick O'Shaughnessy, Sergey Levine
[00:00:00] S0: For people that would want to be fairly technical about following major milestones that are happening in this field, where does that information show up? What do you read to stay informed about what's going on, or watch? [00:00:00] S1: So a lot of it shows up in research papers. Research papers, unfortunately, are not a very accessible source of information because it takes a bit of care to sort through everything and figure out what is the signal and what does something really mean. Research results are sort of intended for an audience that already understands the starting point from all the past research results. Robotics, and I think technology in general, is one of those things where the public-facing artifacts, the demos and the videos that somebody might post on social media, are often actually not very good for providing a sense for the true underlying state of things because they're sort of meant more as a demonstration of the edge of capability and grounding them. What does the demo really mean requires digging deeper. Probably research papers are the way to go. Sometimes even worse than that, you have to actually go talk to the individual people and find out what the inside story really is. And maybe that's not a great situation to be in, but that's kind of how science works. [00:00:00] S0: As we look forward to the future in your mission, what feels the most uncertain?
· Patrick O'Shaughnessy, Sergey Levine
[00:00:00] S0: As we look forward to the future in your mission, what feels the most uncertain? [00:00:00] S1: I do think the timeline is uncertain. If anything, my sense of the timeline has gotten more optimistic since we started, but it's uncertain because of the nature of the technology. This is something where there's a bootstrap challenge. Getting to a particular level of usefulness so that robots can be deployed, so they can do useful tasks, so they can start collecting data from open-world settings at scale. Because that's such a sudden event – getting past the activation energy – I think there is a lot of uncertainty about the timing of that. That's exacerbated by the fact that the timeline looks different depending on what kind of technology is deployed. The example I gave before about whether it should be data collection through teleoperation, or data collection with autonomous systems, or something in between – maybe shared autonomy, maybe this coaching thing. Those all change the picture in terms of how deployments work and how in-the-wild data collection works. So because of that, I do think there's quite a bit of uncertainty. [00:00:00] S0: You're in such an interesting position because you're at the center of research. Lots of different kinds of people are talking to you, asking you questions. What are questions that you're surprised people don't ask you?