When I first interviewed StarBot at NVIDIA GTC earlier this year, the company was demonstrating a wheeled service robot designed for restaurants. We were having a record heat wave that week in San Jose. It was was 93 degrees and we were in direct sun when the little clip below was recorded, which explains the references to water and air conditioning.
Just over three months later, I caught up with engineer Ricky Wu at the Humanity & AGI Summit 2026 at Stanford University, where StarBot was showing something entirely different: a humanoid robot. The transition says as much about the service robotics industry as it does about StarBot itself.
During our interview, Ricky explained that the shift was driven by customer feedback. Restaurant operators liked the original robot but kept asking the same question: could it climb stairs? The answer was no. A wheeled robot is efficient on flat floors but quickly reaches its limits in more complex environments. The company’s response was to build a humanoid platform that could navigate spaces designed for people while adding dexterous hands, depth sensing, LiDAR, and conversational AI.
It’s a reminder that some of the biggest advances in robotics aren’t driven by new AI models. They’re driven by deployment. Real customers expose real problems.
The restaurant industry has become one of the first proving grounds for service robotics, but adoption remains in its early stages. While robot waiters attract attention on social media, the vast majority of restaurants still operate without robots. Where they are deployed, robots typically transport food, bus tables, or handle repetitive delivery tasks, allowing staff to spend more time interacting with customers instead of walking dishes across the dining room.
The labor equation also matters. Restaurants continue to struggle with hiring and employee turnover, making repetitive support tasks an attractive target for automation. Rather than replacing servers, today’s service robots are generally designed to augment existing staff by taking over physically repetitive work.
At the same time, companies appear to be thinking beyond restaurants. In our discussion, StarBot described applications that extended into homes and other service environments. That reflects a broader trend I have begun noticing across multiple robotics startups. Instead of building highly specialized machines for a single market, many companies are evolving toward general-purpose robotic platforms that can be adapted through software and AI for a variety of settings.
Whether that strategy ultimately succeeds remains an open question. A robot built for restaurants must satisfy a different set of requirements than one designed for factories or private homes. But the industry increasingly seems willing to pursue that broader vision.
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