[b9a2cab5]

There exist methods to guarantee robustness within a certain L_p ball on your parameters (in this case the box positions). I'm sure with only a few hundred boxes they would be tractable to solve to optimality.

The actual robotics part is definitely deep learning though.

[Jensson]

> The actual robotics part is definitely deep learning though.

Why? Moving machine arms smoothly has been a thing for a long time.

[candiodari]

If you can provide exact coordinates of every box, then yes, mostly* (including correct guesses of where the box can be grabbed without ripping it. Of course exact coordinates are required even when the box is moving. If it shifts when you're grabbing/lifting/putting it down, you still have complete the action correctly). Or to put it more in engineer wording: it's going to be far more robust to environment changes.

And I would argue that whilst the machine learning way is pretty complex it's still simpler than 3d motion planning of moving robot platforms. And one machine learning solution can adapt to many robots with just retraining, without redoing the formulas from scratch.

* technically on a moving robot platform it hasn't entirely been solved, but good enough solutions do exist.

[Jensson]

Ah, right, image recognition for the boxes. But I don't think they would use it for moving the arm.

> And I would argue that whilst the machine learning way is pretty complex it's still simpler than 3d motion planning of moving robot platforms.

On what grounds do you think this? 3d motion planning isn't complex in these scenarios.

> And one machine learning solution can adapt to many robots with just retraining, without redoing the formulas from scratch.

You don't redo the formulas from scratch, you just plug in the specs of the robot and then you have it. Positioning and moving arm parts is a solved problem. Redoing machine learning for every arm seems much more cumbersome.