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Open Problems in Robotics

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Re: Open Problems in Robotics

#21

Post by Shared404 »

m0zg wrote: post #14
fluffy87 wrote: post #7

Earlier quoted context omitted.

This is the scariest part of using machine learning as an engineer on any practical application as well. Without an error bound, ML can’t be in charge of anything that could put human lives at risk. This is also why I don’t understand all the hype about FSD / L5 autonomous driving. We don’t even know yet if such error bounds even exist, so we don’t even know if machine learning is even the right tool for FSD yet. All…

> Without an error bound, ML can’t be in charge of anything that could put human lives at risk Humans don't have "error bounds" either, and you trust them just fine.

I personally do not trust humans.

However, we do have inertia with humans running such situations, and until there is something provably/demonstrably better, I don't see the current situation changing.

Re: Open Problems in Robotics

#22

Post by mike_mg »

helltone wrote: post #4

One problem I encountered while working on robotics is that many commonly used algorithms yield approximate solutions with no error bounds. They work 99.99‰ of the time. This is fine from a computer science or math point of view, but very scary from an engineering perspective, specifically when there are humans nearby. A big part of me struggles to accept the suitability of algorithms coming from gaming engines or ma…

I find the inverse surprising: that many algorithms that work on real-life robots _do_ _provide_ error bounds and their optimality / convergence properties are proven in the papers that introduce them.

A great example of this is motion planning, where papers both on sample-based methods (such as SST), and on search based (descendants of the A* family) argue at length the theoretical optimality and convergence properties.

On another note, I think requiring more theoretical analysis as a guarantee of safety could partially be an AI-winter meme rather than practical solution. Point in case: do people run a quick check of aerodynamics maths before boarding a flight? No - they rely mostly on the engineering and regulatory process that gradually made passenger flights safer.

Re: Open Problems in Robotics

#23

Post by [deleted] »

jaaron wrote: post #10
fluffy87 wrote: post #7

Earlier quoted context omitted.

This is the scariest part of using machine learning as an engineer on any practical application as well. Without an error bound, ML can’t be in charge of anything that could put human lives at risk. This is also why I don’t understand all the hype about FSD / L5 autonomous driving. We don’t even know yet if such error bounds even exist, so we don’t even know if machine learning is even the right tool for FSD yet. All…

What do you think the error bounds are for a human? I know it sounds like a flippant question, but for certain applications, if we can get a model that's better than human, then it doesn't need to be perfect. And they way we currently do this in all sorts of ways is to pair a human with a computer so that they each do what they're best at. It doesn't have to be about full automation.

[deleted]

Re: Open Problems in Robotics

#24

Post by fxtentacle »

alwahi wrote: post #19
cpgxiii wrote: post #6

In the area of Motion Planning (my own area of research), the most that can be said is that practical solutions exist for a tiny subset of cases, workable methods exist for a larger subset, expensive methods exist for a still larger subset, and everthing else might as well be impossible. - If you've got a low-dimensional problem, say 2D or 3D, without uncertainty (or at least bounded enough to pad obstacles and ignor…

sorry for sidetracking your answer but what actually does convex mean in the context of optimization. I remember looking at a book called convex optimization. Your statement that > Motion planning is very much non-convex, suggests to me that you are very much talking about the same thing. I understand convexity as in a shape. Why is convex good and concave bad in terms of optimization? I don't want you to dumb down t…

Think of numerical optimization (e.g. gradient decent) as if you're letting a marble run down a hill. Only for some hill shapes can you be sure that the marble will reach the bottom. For other shapes, it'll get stuck on a small flat or in a small hole. Those would be called zero gradient or local minima failures.

If your hill has a convex shape, then the marble will always roll up the bottom eventually.

Re: Open Problems in Robotics

#25

Post by tuatoru »

burntoutfire wrote: post #12
fluffy87 wrote: post #7

Earlier quoted context omitted.

This is the scariest part of using machine learning as an engineer on any practical application as well. Without an error bound, ML can’t be in charge of anything that could put human lives at risk. This is also why I don’t understand all the hype about FSD / L5 autonomous driving. We don’t even know yet if such error bounds even exist, so we don’t even know if machine learning is even the right tool for FSD yet. All…

What about "practical error bounds", i.e. testing the system through millions/billions of miles driven?

Miles driven is a useless metric. Stick your vehicle on a treadmill and have it drive a billion miles. What does that tell you?

Edit to add: a better metric would be something like "billions of decisions made where human life was at stake".

Re: Open Problems in Robotics

#26

Post by cpgxiii »

krisoft wrote: post #15

Ugh. This is making a hash of it. What is true: The understanding of laymen about what robots can do is very much out of touch with reality. Making real world robots is very hard. I think it's movies which made people think that any 6 year old can just plop together a C-3PO. What is the hash then? (I omit the points where I lack experience.) 1; Motion planning "Even things like a model of where the robot is, with res…

Depth estimation

I wish it were as solved as vendors would like to believe it is. If you want a sensor for medium-range applications, say order 0.5m to 10m depth with better than 1cm accuracy, that works indoors and outdoors, with reflective and untextured objects, doesn't interfere with other sensors of its kind, it simply does not exist. Traditional active and passive stereo is OK provided you have texture, ToF so long as the surfaces aren't reflective and you don't have sunlight to worry about, structured light if you can control lighting and can otherwise control/avoid interference from other sensors. There's some promise in learned stereo matching, but collecting enough data and running fast enough inference are big challenges to practical use.

Existing depth sensors for manipulation tasks, even indoors in reasonably controlled lighting, are still mostly insufficient given that the objects they struggle to see are often the objects you want to manipulate.

Re: Open Problems in Robotics

#27

Post by foerbert »

m0zg wrote: post #14
fluffy87 wrote: post #7

Earlier quoted context omitted.

This is the scariest part of using machine learning as an engineer on any practical application as well. Without an error bound, ML can’t be in charge of anything that could put human lives at risk. This is also why I don’t understand all the hype about FSD / L5 autonomous driving. We don’t even know yet if such error bounds even exist, so we don’t even know if machine learning is even the right tool for FSD yet. All…

> Without an error bound, ML can’t be in charge of anything that could put human lives at risk Humans don't have "error bounds" either, and you trust them just fine.

Humans are capable of generating and understanding creativity and complexity that are simply impossible for non-AGI automation. Even then, we don't just let people figure things out for themselves. We put them through training, and then test them. Even after that, we make them liable for negligence.

I don't think it's an obvious conclusion that error bounds aren't important for automation because they aren't calculable for a human. They are just very different beasts.

Re: Open Problems in Robotics

#28

Post by cpgxiii »

tuatoru wrote: post #20
cpgxiii wrote: post #6

In the area of Motion Planning (my own area of research), the most that can be said is that practical solutions exist for a tiny subset of cases, workable methods exist for a larger subset, expensive methods exist for a still larger subset, and everthing else might as well be impossible. - If you've got a low-dimensional problem, say 2D or 3D, without uncertainty (or at least bounded enough to pad obstacles and ignor…

But we don't need optimality or even analytic solutions for practical robot motion. Do we?

Of course we don't need full optimality for most problems, that's why RRTs and PRMs have proven useful on practical problems despite (RRTs especially) providing obviously suboptimal solutions. The problem is that really suboptimal solutions are bad (take too long, require too much energy, look scary to humans, etc) and we'd often like "OK-quality" solutions that require some sort of smoothing or optimization on top of an otherwise suboptimal solution.

There is a class of problems where optimality is super-important, where feasibility isn't a binary yes/no and you must minimize some objective. For example, in surgical robotics, you'd like to plan a path that minimizes deformation of tissue, and that requires some sort of optimality.

The perfect solution, of course, is something like A* that is complete and optimal, but that remains out of reach for higher-DoF problems.

Re: Open Problems in Robotics

#30

Post by tuatoru »

cpgxiii wrote: post #28
tuatoru wrote: post #20

Earlier quoted context omitted.

But we don't need optimality or even analytic solutions for practical robot motion. Do we?

Of course we don't need full optimality for most problems, that's why RRTs and PRMs have proven useful on practical problems despite (RRTs especially) providing obviously suboptimal solutions. The problem is that really suboptimal solutions are bad (take too long, require too much energy, look scary to humans, etc) and we'd often like "OK-quality" solutions that require some sort of smoothing or optimization on top o…

Thanks for the exegesis!
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