90% of robot prototypes never make it to real factories.
They work in a closed lab. They look impressive on video. And then reality hits.
In this episode, we break down what actually separates a convincing prototype from a system that runs reliably in production. And why that gap is much harder to close than most people think.
You'll gain insights into:
- what makes a prototype fail in real deployment
- why 99% reliability is harder than it sounds
- how the digital twin works inside a neural net
- where humanoid robots really stand today
More about RobCo:
Website: https://www.rob.co
LinkedIn: https://www.linkedin.com/company/robco-therobotcompany/
Instagram: https://www.instagram.com/robco_therobotcompany/
Chapter markers
00:00 Intro
02:00 Why robot prototypes are often misleading
05:41 Reliability beats impressive capabilities
07:46 Why RobCo builds end-to-end solutions
09:27 48-hour testing & real-world data loops
12:23 How closed learning loops actually work
14:09 Digital twins explained simply
17:49 The digital twin as a map of the real world
19:57 How physical AI filters relevant information
22:32 What people will misunderstand about physical AI
24:51 Humanoid robots: hype vs. reality
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