Inside Agility's bet on factory-first humanoids

Agility Robotics CTO Pras Velagapudi said the company is betting on factory floors as the proving ground for humanoid robots, citing a massive labor shortage and repetitive, dangerous tasks. Agility has already deployed robots with Toyota, Amazon, and Mercado Libre, and Velagapudi emphasized that humanoids can fill the 'islands of automation' left by fixed robotic arms and mobile robots.

Humanoid robots continue to capture the imagination of humans, and the recent AI revolution has only accelerated the momentum. But despite demonstrations of robotics tidying up homes https://x.com/sundayrobotics/status/2077807434782097435 or awkwardly dancing on stages https://x.com/adamcurtisbroll/status/2057050384166764826 , there may be a better place to start: Factory floors, Pras Velagapudi, CTO of Agility Robotics, told The Deep View. With a massive labor shortage and many repetitive, dangerous tasks, manufacturing may be the perfect proving ground for humanoid robots. It's why Agility has focused on this niche, and already has robots deployed with companies such as Toyota, Amazon and Mercado Libre. Velagapudi sat down with The Deep View in August at Ai4 in Las Vegas to discuss how humanoids can solve the "islands of automation," how these robots could shift the blue-collar labor force, and the path to robots eventually becoming household tools. This interview has been edited for brevity and clarity. Rubio-Licht: Something that I've been drilling down into the more I research physical AI is the humanoid form factor. Why do you think this is the right bet? Velagapudi : We've been working on it for about a decade, this idea of core loco-manipulation. This is a spinout of Oregon State University, built on research from Carnegie Mellon University. We have a long history of trying to get robots out in the world. Now, what's accelerated in the past few years is that we've kind of cracked all of the core technologies necessary to do this. We had been working for a long time on the physical hardware, the controls necessary to do that, and we had been making good progress and basically got all those pieces together. And then the physical AI boom happened, and has been carrying us on this wave of capability that, combined with all the hardware that we've been able to put together, is really making it so that we feel like this is a compelling time for this particular platform. It's a compelling time for humanoids because all the right factors are there to enable them right now. Rubio-Licht: And why are humanoids right for manufacturing versus, say, robotic arms? Velagapudi : It isn't really humanoids versus robotic arms. It's more that manufacturing is a huge industry. One of the reasons we're going after it is because it's such a huge industry that even with robot arms and Autonomous Mobile Robots , ground robots moving around and welding robots and all of these different pieces, there's still so much that hasn't been automated yet. What we call that is islands of automation. That there are these pieces of automation that are then separated by some sort of manual process, and then another piece of automation. But there are these little islands that exist that need to be connected by some manual process. Someone unloads a thing and puts it onto another thing. They take it off of a shelf. They put it onto an Autonomous Mobile Robot . They take it off of an Autonomous Mobile Robot . They put it into a conveyor belt. And so there's still so much of that that's been difficult to automate because it has the wrong structure for a robot arm, or it has to occur in many different places in a facility. So you can't build a fixed automation station around it. All of these things make it difficult, but are quite well adapted to the one form factor that's been doing it this whole time: The human-centric form factor. Rubio-Licht: How do you think robotics are going to eventually impact the labor force? Velagapudi : What we're taking on are really the dull, dirty, dangerous jobs — the three D's of robotics — and we're very focused on areas like bulk material handling, picking up 50-pound totes, putting them into loading and unloading machinery. This is in hot environments, in dangerous environments where there's risk of repetitive strain, where you're working around other machinery. So what we're doing with these deployments is really trying to get those jobs filled — the ones that are this type of manual labor that's not really well-suited to humans. Let's clear those off so that the human labor can be concentrated into areas where it can be upskilled jobs, supervising, managing the fleets of robots. This introduces new roles like robot technician and robot supervisor and robot operator. Jobs that can be done remotely, or can be done from an office. They don't involve someone necessarily having to be out on a factory floor or a warehouse floor for extended periods of time. We're trying to move and upskill the labor that we have to allocate it to where we want it to be and get it off of the backs of these really menial jobs where there is high turnover and high repetitive strain. Rubio-Licht: What sorts of challenges is Agility running into in training these machines? Velagapudi : When we're deployed with customers, it becomes about reliability and safety. In a lab environment, performance is about how well you can tune everything to be as fast as possible and as efficient as possible in your constrained environment. But once you get out to a facility, there's all these other factors. There are robots that might get worn or damaged over time, or material that's damaged over time. We get broken or jammed together totes, or we have labels that are ripped off. We have equipment that we're loading, and the equipment will go down. All of these now become the challenges that you have to address Also, when you're deploying out into a manufacturing environment, there's also quite stringent safety regulations and expectations. We're not just doing it because it's the legal thing to do. We're doing it because it's the ethical thing to do when you're deploying robots. So when we're trying to build a safe robot deployment, that means taking into account not just is the robot going to do something unsafe, but also can the robot or its environment have a failure or deviation that could lead to something unsafe happening, and how can we design against those things. That might involve putting up guarding or having the onboard safety controller on the robot trigger when certain conditions are met, such as detecting that an unsafe condition has happened, or that an emergency stop has been triggered somewhere in the system. Rubio-Licht: One challenge I've discussed a lot in conversations about robotics is this idea of the data gap. How has that impacted Agility? Velagapudi : This is one of the things we have to address as we scale up. The data gap that exists is a limiting factor for the speed at which we can develop our models that cover new skill sets, but I wouldn't say it's necessarily a hard blocker. For one thing, within manufacturing in particular, there's a lot of fairly repetitive and constrained tasks, and for those, you don't need as much diversity of data. You still do need diversity, but it doesn't necessarily have to be at the same order of magnitude as, for example, dealing with all household items. That helps us out. It means we can get deployed right now. It just is into a more constrained set of tasks, and as we get better with our models, that set of tasks expands. Now, getting that set of tasks to expand is still facing this problem, but I think what we've observed now is that people are coming up with ways to address this gap. That is leveraging human and egocentric data. It is building up cross-embodiment data, which is collected on many robot bases, and pre-training models around those. It's also just more data-efficient techniques for foundation models. So we are figuring out the recipe, and I think we figured out quite a bit of it. Now, I'd say, the frontier models for this type of activity are getting pretty good, even with the limitations on data that we have. Rubio-Licht: What sorts of other tasks or domains do you see Agility expanding into? Velagapudi : The expansion path is actually following the safety path. Where we can get the safety and regulatory compliance that we need really is defining where we'd go next. Manufacturing and logistics falls under industrial robotic safety standards. The next step is getting into service robotics, and that starts to unlock things like retail and healthcare, and so that's kind of the progression that we see. But the interesting thing is that the actual skills that you build aren't necessarily constrained to just manufacturing. All of the pipeline for building a model that's really good at a large variety of manufacturing tasks can be reused for a large variety of e-commerce tasks and a large variety of back-of-house retail tasks. The pipeline is the same pipeline, and the models actually benefit from getting trained on more diverse use cases. Rubio-Licht: Do you anticipate Agility expanding into the household domain? Velagapudi : It's definitely in the future. We do want to get there. We do think that one of the great untapped markets for humanoid robots is closest to humans, like in homes, in hospitals, care facilities, things like that. It's where there's a lot of value because you have the most human-centric environment. There, you have the benefit of creating a robot that can do all the things in your environment that you do in your environment, but the road there to do it safely is going to be a bit longer. I think we'll either start out with robots that have simplified capabilities but are easier to make safe, and increase those capabilities over time, or it'll simply be that other industries will be the first ones to adopt these platforms, and then we'll see it move towards the home over time. Rubio-Licht: I feel like the road to something as unpredictable as any person's apartment or house could rely on achieving generalization or so-called physical AGI. How is Agility considering this? Velagapudi : I think that it's probably harder than people give it credit for to get that far. But I think that we can get to handling a lot of things pretty effectively. What we'll get to is models that'll handle a lot, but not all of the tasks, and that'll be okay. At some point, it'll be sufficient for the consumer market to say "this is good enough." A good example of this is the Roomba. When you buy a Roomba, one of the first things that people end up doing is they start to tidy up all the junk that they've been leaving on their floor. Like all of a sudden, a bunch of cables and random bits and pieces get pulled up off the floor. You basically Roomba-proof your home to make the robot work. And so I think there'll be a little bit of that, where once there's a sufficient value proposition, you'll make your room robot-proofed, so that you can bring it in to do all of this other stuff. Getting maybe 70% of the way there might be good enough.