Roboticist here. All of this, and he didn’t mention compliance or online adaptation to otherwise un-sensable dynamics. Or massively complex miniature mechanisms.
Current generation tactile sensors cost a couple thousand $ PER FINGER, and have a real world MTBF of hours. The cost can be solved with economy of scale. The fragility is harder.
Curious on your take on this, given your domain - is the hardware the principle challenge in your opinion, or the software?
> It will be difficult to match this scale of breadth and depth of data for physical tasks. There’s no straightforward equivalent of “just Efficient learning, generalization, and adaptability / on-the-job learning seem like requirements.
Isn’t this the idea of NVIDIA’s Isaac? Model based adaptive learning in virtual environments for robotic systems? Or is this oversold?
Current generation tactile sensors cost a couple thousand $ PER FINGER, and have a real world MTBF of hours. The cost can be solved with economy of scale. The fragility is harder.
Are we talking about experimental laboratory ones here? What happens when the Alibaba players start getting into the game? They have plenty of humanoid robots.
Capacitive touchscreens are not tactile in any way whatsoever. They are purely electrical, and there are plenty of things (actually, most things, I'd say) which are not sufficiently capacitive to be detected by such approaches. You usually need to measure physical deformation instead, which is much more difficult to do precisely. (There are capacitive approaches to that, but they don't work like a touchscreen)
I don't think that's true. AI could work around all the physical imperfections.
Just imagine yourself controlling an imperfect robot with some joysticks or maybe a sensor suit. You'd certainly do much better than current robots. The limit is the robot's brain, not the physical actuators/sensors.
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Roboticist here. All of this, and he didn’t mention compliance or online adaptation to otherwise un-sensable dynamics. Or massively complex miniature mechanisms.
Current generation tactile sensors cost a couple thousand $ PER FINGER, and have a real world MTBF of hours. The cost can be solved with economy of scale. The fragility is harder.
Curious on your take on this, given your domain - is the hardware the principle challenge in your opinion, or the software?
Isn’t this the idea of NVIDIA’s Isaac? Model based adaptive learning in virtual environments for robotic systems? Or is this oversold?
Are we talking about experimental laboratory ones here? What happens when the Alibaba players start getting into the game? They have plenty of humanoid robots.
i think you can get some good surface for a few hundred $$ / few fingers here
https://xelarobotics.com/
How much more sensitive do you need to be compared to say a standard mobile capacitive screen and those are essentially dirt cheap at this point.
Capacitive touchscreens are not tactile in any way whatsoever. They are purely electrical, and there are plenty of things (actually, most things, I'd say) which are not sufficiently capacitive to be detected by such approaches. You usually need to measure physical deformation instead, which is much more difficult to do precisely. (There are capacitive approaches to that, but they don't work like a touchscreen)
I'm picturing humanoid robots having to operate in pairs so they can constantly fix each other.
Also so they can watch each other, in case of the humans trying to shut them down.
That's why the human operator needs a gun. :-)
i like this
yep. and with the western funding model, how many hardware iterations can you do before you run out of runway?
I don't think that's true. AI could work around all the physical imperfections.
Just imagine yourself controlling an imperfect robot with some joysticks or maybe a sensor suit. You'd certainly do much better than current robots. The limit is the robot's brain, not the physical actuators/sensors.
(I'm not saying there couldn't be improvements in the area of the actuators)