From Prototype to Production: Why Most Robots Never Leave the Lab Focus

Robotics has reached a point where technical ambition is no longer the hard part. The harder part is repetition. A robot that can complete a task once in a controlled setting may still be nowhere near ready for manufacturing, field testing, service, or scale. That distinction matters more now because robotics is no longer a narrow research conversation. The International Federation of Robotics reported that 542,000 industrial robots were installed globally in 2024, more than double the level from a decade earlier. Demand is expanding. The bar is rising with it.

Few leaders work closer to that line than Clayton Haight, Director of Robotics Hardware at Sunday Robotic. Across roles at Tesla, Zipline, Neuralink, Matician, and now Sunday, he has worked on electromechanical systems that had to survive more than a good demo. They had to survive design reviews, manufacturing constraints, assembly realities, supplier coordination, and the unforgiving economics of physical product development. He has also been recognized in broader founder and innovation circles through his role as Judge at Conviction, a thesis-driven firm that invests in disruptive technologies such as AI, data platforms, and frontier infrastructure, and a fitting detail for someone whose perspective is rooted less in robotics theater than in build discipline.

We sat down with him to talk about why so many robots stall after the prototype stage, what production-ready robotics actually requires, and why hardware teams that think like manufacturers tend to outlast teams that think like research labs.

Clayton, why do so many promising robotics systems fail to progress beyond the demo stage?

Because a demo usually proves possibility, not repeatability. That is the first misunderstanding. A team gets a system to perform a narrow task under supervised conditions and starts treating that moment as evidence that the product is ready to move forward. In reality, the demo may only confirm that the robot can work with hand-tuned calibration, carefully selected parts, and operators who already know where it breaks.

Production does not care about any of that. Production asks whether the same behavior still holds when tolerances vary, when parts arrive from different lots, when assemblies are built by more than one technician, and when the robot has to survive repeated use rather than a single successful run. That is where a lot of robotics programs begin to thin out.

Research in robotics and production robotics are solving different problems. Research is trying to prove that a behavior is achievable while production is trying to prove that the behavior can be built, tested, assembled, supported, and reproduced without the entire system becoming fragile or uneconomical. Many teams are much further along on the first question than the second.

That gap shows up quickly in wearable robotics too. On Sunday’s Skill Capture Glove platform, the challenge was not only whether a sensing concept could function. It was whether the hardware could support long sessions, preserve ergonomic comfort, maintain calibration, and collect consistent multimodal data across repeated use. Once you start asking those questions, the work becomes much less about novelty and much more about discipline.

What does it really take for a robot to be production-ready?

It means the robot has moved past technical feasibility and into reproducibility. A production-ready robot is not simply a machine that works in the lab. It is a system whose behavior can be recreated through a controlled manufacturing process with predictable quality.

That includes architecture choices that reduce unnecessary complexity, parts that can actually be manufactured with acceptable yield, assembly paths that do not create avoidable risk, and test procedures that can catch failure before the system leaves the build floor. It also means serviceability matters. If the robot breaks, can the team diagnose it, repair it, and return it to operation without turning every field issue into a redesign exercise?

On the Skill Capture Glove work, that meant treating manufacturability as part of the design process rather than as cleanup at the end. I was directly responsible across mechanical design, system integration, firmware, and manufacturing readiness, so the job was never just to get the glove working once. We used CAD and rapid 3D printing to move through two to three prototypes a day, then pushed those iterations through user testing to refine ergonomics, sensor placement, and structural reliability. That is where DFM and DFA stop being abstract terms. They become the difference between a prototype that impresses the room and a device that can actually support a growing fleet.

The robotics industry is beginning to feel that distinction more sharply. The IFR reported that professional service robot sales reached nearly 200,000 units in 2024, up 9% year over year. As systems move into more real operating environments, production readiness stops being a nice-to-have and becomes the condition for commercial credibility.

Why is hardware reliability in robotics fundamentally harder than software reliability?

Because hardware failures compound. A software issue may sit inside a logic path, a workflow, or a state transition. In robotics, the failure can come from interaction between the mechanical stack, the electrical system, sensor drift, connector fatigue, cable routing, calibration error, thermal behavior, or assembly variation. Sometimes each subsystem looks acceptable on its own and the system still fails because the interaction between them was not understood well enough.

The other reason is that hardware feedback loops are slower and more expensive. If a software team finds a bug, it can often patch and redeploy quickly. If a robotics team finds that a part is deforming, a connector is failing, or a calibration process is too sensitive, the fix may involve rework, supplier changes, tooling adjustments, another validation cycle, and delayed builds. Physical systems punish late learning.

The same gap shows up across the broader physical AI ecosystem as well. At Physical AI Hacks 2026, it was easy to see how many systems looked strong in concept but weakened once execution constraints entered the picture.
That was part of the progression from the earlier Skill Capture Glove builds into the next-generation version as well. The work had to improve sensing precision and durability while also tightening manufacturability and deployment practicality. In robotics, better hardware is not just better performance. It is better consistency.

Where do early robotics startups usually go wrong when they try to scale hardware?

They wait too long to act like a manufacturing organization. That is the central mistake.

A lot of teams freeze industrial design before they have done enough DFM and DFA work. They use too many custom parts too early. They treat suppliers as downstream vendors instead of technical partners. They underestimate tolerance stack-up. They delay test infrastructure until late builds. They design for a successful demo rather than for assembly, service, or sustained use.

That logic breaks down fast once a system gets more complex. On Lemi-2, the prototype that became Sunday’s strongest public expression of the Memo robot, the work was not just about hardware performance. It was about cross-functional execution. I was the DRI on the project, which meant coordinating engineering teams, timelines, and suppliers while managing risk as it emerged. The system had to become more reliable than the previous generation and available at higher volume for research, internal use, and external product testing. According to the project data you shared, researchers moved onto a platform that was three times more reliable than Lemi-1 and the team built eighteen times more robots than previously available. That is what scale pressure looks like in practice.

The public version of that story became visible when Wired covered Memo in November 2025 as Sunday prepared to move the robot from controlled demonstrations toward beta testing in real homes. The coverage highlighted the company’s attempt to build a home robot capable of handling messy household environments rather than tightly bounded industrial tasks.

How do you design robots that can actually be built and deployed at scale?

You start earlier than most teams think. Scale is not something you add after the prototype works. It has to be built into the design logic from the beginning.

That means reducing unnecessary part count, simplifying assembly paths, choosing processes that can support volume, designing calibration and test access into the hardware, and building validation cycles around the conditions the robot is actually likely to face. It also means accepting that manufacturability is a design requirement, not a downstream function. If the robot only works when every variable is hand-managed, then the design is still incomplete.

The industry is moving into a phase where that distinction is becoming harder to avoid. The IFR said in January 2026 that the global market value of industrial robot installations had reached an all-time high of $16.7 billion. Around the same time, Reuters reported that generalized physical AI was beginning to appear in early commercial settings, but also noted that reliability, safety certification, and cost remain major barriers to large-scale deployment. That is exactly the pressure point now. The next wave of robotics will not be decided by which teams can produce the most exciting demo. It will be decided which teams can build systems that survive production economics.

That is also why community-building matters. Events like Autonomous Robot Build Day are valuable not because they celebrate robotics in the abstract, but because they keep the field close to the practical act of building. In the end, that is where the industry’s real filter sits. Robots do not leave the lab because the lab is where possibilities are proven. They leave the lab only when engineering, manufacturing, and validation are strong enough to prove repeatability.