In robotics deployment, the most critical success factor is ensuring that a single robot's performance can be reliably replicated across thousands of units without requiring rework or redesign, as demonstrated by Figure AI's experience where the same robot that started a six-month shift was still operational at the end, proving the system's scalability and consistency.
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Brett Adcock on the Hard Lessons From Figure AI's Early Real-World Deployments #Shorts #FigureAI #RoAñadido:
we were a piece of that.
And um the goal was just like, can we run robots every day? You know, I mean are we going to get our like ass handed to us? You know, is it going to be easy? Is it going to be hard? And uh I think it was in the middle. Like I think we like um we we got the robot to a a great spot where it was running every day, it was great. I think the biggest learning lesson we took away is we had we really I really cared about if can we do that and can we like clone it times a thousand, times ten thousand? Would we have any issues scaling?
That was the part for me. Like was it just like, you know, does it completely the bed and he's like, we need to rework our plan and go back to the office? Does it do incredibly well and you can just copy paste this thing everywhere in the world? Like how do how does it how did it work? And the biggest learning lesson we got was that the robots the robot that started the first day at the start of six months and the robot that ended the shift that day was the same like even though we had multiple robots in operation at
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