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I worked for a startup developing robot arms for a while. What we found was that giving someone a robot arm - even one with reasonable APIs and no cost to them - didn't really help because the hard part is making useful automations. Mostly people spent a few hours playing with the arm and then put it on a shelf.

Every use case is completely different and is a lot of work. Even when you get something working, accidentally shake the desk or crash the arm into something and all your coordinates are broken and you have to start again.

Not to mention the actual mechanics are really complicated - to have a meaningful payload at 50cm reach you end up with really high torque at the base joints (which also need to have super-high accuracy), which requires expensive gears and motors. None of that is cheap.

Then you get to safety - an arm that has a useful payload is also quite heavy, and having that amount of mass flailing around requires safety systems - which don't come cheap.

It's a bit like hardware no-code - you can't make an easy to use robotic arm because programming it is inherently hard. I think the only thing that will change that is really good AI.



In Robotic assisted surgery, Tracking targets are mounted on the robot arm and an infrared camera tracks the exact position.


It's cool to hear from someone with experience!

Do you know if anyone has tried building an arm that uses spatial positioning techniques from augmented reality, like structured light or pose tracking[1], to understand the position of the arm in space without resorting to "dead reckoning"?

It seems like that kind of approach would increase the physical tolerance and reduce the programming complexity, since you know both a) where the arm is supposed to be, and b) where it actually is.

[1] https://en.wikipedia.org/wiki/Pose_tracking#Outside-in_track...


This is actually pretty common. But getting enough resolution to improve on what you can do with the encoders isn't so easy.

The more usual application of multi-camera setups etc. is in path planning and scene understanding, not low level control.


Yep. Interestingly, there has been a lot of recent work on models like RT-2 that might be capable of automating this for simple tasks. We might be at the point soon where that startup would have been viable!


AFAIK RT-2 doesn’t quite work outside of Googles micro-kitchen, where they collected about a 1000 hours of data.


That's how prototype tech often work : not very well. But its a proof of concept. A general model is probably not terribly far off.




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