[imtringued]

I think you're misunderstanding the essence of the problem. If you use tendons, you'll need a neural network in your control loop that can learn continuously so that it compensates rope stretch and changes in friction through wear and tear.

A lot of problems in robotics reduce down to continual learning. Essentially all system identification tasks become obsolete the moment you have a self learning system and yet we have an AI industry preaching that AGI is around the corner without this "crutch".

[stefanka]

Not to mention that they easily break and are annoyingly tedious to be replaced (I had to assist often enough on such "surgeries" in our lab)

[stefanka]

You don't necessarily need neural networks for that, there are more specialized function approximations that learn faster and enable life-long learning for kinematics/dynamics and extrapolate better than NN which are more general purpose.

[amelius]

You could add encoder patterns to the tendons, like stripes. And then use something like a mouse-sensor to track them. This is still much lighter than servos.

Another idea is to use an external camera or two and to track the fingers with a deep learning model. But this can become messy if other objects are in view. And it might also introduce more control delay in the feedback loop than a simple sensor.

[stefanka]

If space permits you should add absolute encoders in the joints. In a finger that’s likely a challenge. In an arm, that’s easy

[spauldo]

I would think pressure and angle sensors tied to a PID loop would suffice, but I'm not a roboticist.

[stefanka]

If you can get a feedback loop at the respective joint, then yes, this might be enough. In case of an arm (not this hand), you can often only observe the end of a chain, and then, that’s more complex