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CreateMe, an automated soft materials manufacturing company, has created a system that uses physical artificial intelligence to revolutionize the way textiles are manufactured. To do this, the company is replacing traditional sewing, which is particularly difficult to automate, with digital bonded structures that leverage robotics, proprietary adhesives, and an AI-driven manufacturing system.
The company says its modular engineering robot assembly system is the world’s first autonomous tailoring platform that combines hardware and software, “accelerated by physical AI to deliver dynamic variability and flawless precision.” However, challenges remain, especially when it comes to handling cloth, which is not stiff and difficult for robots to handle consistently.
CreateMe CEO Cam Myers has been awarded 25 patents for apparel automation technology developed by his company. Prior to founding the company, he was part of the founding management team of Group Commerce, a venture-backed e-commerce platform that was eventually acquired by Blackhawk Network, and previously held positions at DoubleClick and Allen & Co. He holds bachelor’s and master’s degrees from the University of Cambridge and an MBA from Northwestern University.
In a recent interview with Manufacturing Dive, Myers spoke about the current state of physical AI, how some companies are starting to integrate the technology into their manufacturing processes, and the steps other companies can take to see if it works for them.
This interview has been edited for clarity and brevity.
Manufacturing Dive: How is your company currently using physics AI? What are your plans for the future?
Cam Meyers: We’re using some kind of basic physical AI model, but we have a very unique perspective because we’re focused on deformable materials. Like this humanoid, you don’t have to move horizontally, like washing the dishes, then folding the laundry, then taking out the garbage. We need to be very good within narrower constraints. How to handle the fabric [including] Type of fabric.
We use some basic models, but we specialize in specific use cases…I think this is where rubber comes into play in manufacturing as a narrower application of physical AI…so it’s not just point cloud to point cloud positioning of mobile robots. It can still be non-deterministic, but within a range of more constrained outcomes.
Following this principle, we collect data in three ways. We have a typical remote operation. Our molds are semi-automated using human operators; humans do the work. Place the fabric in the mold and fold it to create what is essentially a neckband found on a crew neck. [T-shirt]. we [also] There is an actual human operator [manufacture a] The product…has captured training data, supplemented with two other data channels.
[We also] It has a data channel with remote control that mimics the same motions a human operator would make to place fabric on a device as a robot would. And we’ve developed an interesting handheld grip technology…inspired by medical applications for robotic hands.
We have this training data and at the end of the year these roadmaps will converge and at that point we can start removing steps that still have human operators.
What do you think about other manufacturers using physical AI?
In my opinion, physical AI will be more tangibly implemented within the next three years in manufacturing, where the ROI is highest. What I’m really saying is that we’re going to see more adoption in narrower applications in the short term.
I think some of these more general purpose humanoid robots are a little further down the line. of [best] Opportunities for physical AI applications compared to other types of more traditional robotics and automation in the industry include: [physical AI] It can still start to become a little non-deterministic, but it’s not completely unlimited.
These tools must be selected and placed. But the next evolution will see a significant increase in variability. —For example, a warehouse. It’s just a matter of finding the right springboard.
How will physical AI impact the manufacturing workforce?
I think this is an opportunity from a human resource development perspective. [that] More mechanical and monotonous types of tasks will be taken over by automation. Also, from a productivity standpoint, I think we will be able to increase the number of technicians we can support. [a given] Number of rows.
So, for example, one of our more streamlined lines only requires half as many technicians per line. One technician handles two lines. From a productivity perspective, the scalability factor becomes important. Humans will continue to be absolutely indispensable.
U.S. manufacturing needs to focus on being flexible, volatile, and responsive to market. And it’s not just apparel. Throughout manufacturing, those closest to consumption and closest to distribution centers are desired to have the highest variability. So I think it’s important to have a really strong workforce there. And some of the physical AI automation will help scale these people up to support a more diverse range of services across markets.
In terms of employment, I think there will be more workers at the technical level and perhaps fewer at the production level. What I’ve seen is that various industries are considering moves to reduce risk in their supply chains. [and] There is production capacity onshore, but frankly it hasn’t existed for over 30 years.
You mentioned that the AI boom is no longer just about chips, models, and data centers, but also about rebuilding the physical industrial infrastructure needed to support them. Could you please elaborate on that?
I think many industry organizations are participating. [be] I support that effort. We are a founding member of Robots for America, a consortium that includes a range of robotics companies as well as industrial manufacturing companies. Other industry groups, such as the New American Industrial Alliance, [rebuilding physical infrastructure].
Industry said we need to drive this because it requires a joint effort with different departments of government. Because if you look at China’s offshore, you’ll see that it’s very much a top-down effort. So I think we’ll see more stimulus for the industry to work with the government.
Another is the growing scope for physical AI to emerge across manufacturing environments, and in the dynamism of new companies entering the space from an industrial and manufacturing technology perspective.
What’s been interesting over the last few years is the go-to-market model of some of these companies, whether they’re in defense technology, space technology, or other heavy industrial environments. [There] Rather than selling industrial technology, which may use AI, to another large industrial manufacturing OEM, the focus is on selling the product to the end user, whether it’s a government or a car company.
What steps should manufacturers take to begin incorporating or expanding the use of physical AI into their operations now and in the long term?
I think the biggest opportunity for American manufacturing lies in Hyflex. [and] There are clearly significant benefits to simulating various production models… There are consulting firms that work with large software companies that build simulation software [and] We help small and medium-sized manufacturers explore digital twin simulation and decide where to invest. Obviously there are still a lot of steps to be taken, but I think this is a concrete way to consider things like ROI and trade-offs before spending big bucks on robots and software.
