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Open Problems in Robotics

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Re: Open Problems in Robotics

#91

Post by YeGoblynQueenne »

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…

Convex/ non-convex optimisation refers to the shape of the error function we're trying to optimise. In convex optimisation we can assume it's, well, convex:

  .               . ε
   \             /
    \           /
     \         /
      \       /
       '._ _.'

          ^
    Global optimum
In non-convex optimisation we can't make any assumption about the shape of the error function:

      ,--.            .--.                               ε
     /    \          /    \              ,--.
    /      \        /      \            /    \        /
  .'        \      /        \          /      \      /
             \    /          \        /        \    /
              `--'            \      /          `--'
               ^               \    /            ^
               |                `--'             |
               |                 ^               |
               |                 |               |
               |                 |               |
               `--------- Local optima ----------'
"Optimisation" means that we're trying to find an optimum of a function - a maximum or a minimum. We're usually interested in the minimum of a function, particularly a function that represents the error of an approximator on a set of training data. Generally we prefer to find a _global_ minimum of the error function because then we can expect the resulting approximator to generalise better to data that was not available during training.

If the error function has a convex shape we're basically guaranteed to find its global minimum. In non-convex optimisation, we're guaranteed to get stuck to local minima.

(Ok, the above is a bit tongue in cheek, there's no _guarantee_ of getting stuck to local minima, but it's very likely).

Re: Open Problems in Robotics

#92

Post by nwatson »

NalNezumi wrote: post #87
deepGem wrote: post #77

Earlier quoted context omitted.

"The real problem: solve any of these problems, make very little money" - Just curious why have you come to this conclusion ? Object manipulation has potential products in dishwashing and vegetable chopping - sufficiently large markets, potential billion $ outcomes for a startup which takes the early mover lead. Two robotic hands that can work in co-ordination just as human hands do. Extremely difficult to solve, but…

I'm not the OP, but the real issue in robotics is two-fold and he mention both. 1. The cost & technical limitation. The cost, as some of the solution per problem OP suggest include "Lidar, visions and sensor" and all of those thing cost a lot by themselves. On top of that you need good actuators (Harmonic drives etc) for precision, and then good computing unit to handle all of it. Now you also need more power supply,…

Hmmm, lawyers, they will be involved. Add astronomical insurance and liability costs when things do go wrong in less-constrained and less-controlled environments.

Re: Open Problems in Robotics

#93

Post by YeGoblynQueenne »

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 don't have "error bounds" either, and you trust them just fine.

Yes, but humans are already here and doing all those dangerous and difficult things. But if we're going to replace them with something, it makes sense to want to replace them with something that's better than them. I mean us.

Otherwise, we might as well stick with the humans. Especially since we already know how to make humans (whereas self-driving cars, not so much).

Re: Open Problems in Robotics

#94

Post by azernik »

mikro2nd wrote: post #59
contingencies wrote: post #52

Robotics founder here. Popular conceptions of "robots" are unrealistically general. In industry, we do not build robots, we build automation systems. Given the choice, would you prefer an automation system with some environmental assumptions, high speed and perfect repeatability (ie. entire industrial automation world), or no environmental assumptions, crushingly high cost, slow speed and poor reliability (eg. walkin…

Depends what you're after, I guess. If all you want is a 'slave' that does some repeatable action with specified accuracy, then an automation system is just the thing. If you're after exploring the questions around what it means to be an autonomous, sentient being, then perhaps not so much and you really want to build a robot.

The money (and hence most of the engineering time) is in making people's lives and jobs easier, not in exploring deep philosophical questions.

Re: Open Problems in Robotics

#95

Post by azernik »

imtringued wrote: post #31
burntoutfire wrote: post #12

Earlier quoted context omitted.

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

Empirical evidence has shown that all Tesla cars have the same failure mode when a stationary obstacle is on the highway. This is not some fringe failure that happens because of wrong classification, it's how the Tesla Autopilot was designed to function. Tesla has claimed explicitly that it's the driver's responsibility to avoid such a situation.

Tesla is not a good representative of the field; they are waaaay behind Waymo (Google) and Cruise (GM).

Re: Open Problems in Robotics

#96

Post by Autowired »

Animats wrote: post #66

- Motion planning: already discussed. - Multiaxis singularities: much less of a problem than it used to be. We don't need closed-form solutions any more; we have enough CPU power at the robot to deal with this. You need some additional constraint, like "minimize jerk" when you have too many degrees of freedom. - Simultaneous Location and Mapping. SLAM for short: Getting much better. Things which explore and return a…

Do you think depth estimation could be done with two cameras plus computer vision (to find markers)? I think this is more or less what we do with our own eyes. Of course you would need much more processing power, but maybe for some applications the robot's brain doesn't need to be inside its body.

Re: Open Problems in Robotics

#97

Post by YeGoblynQueenne »

m0zg wrote: post #60
foerbert wrote: post #27

Earlier quoted context omitted.

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 calculab…

My point is not that it's not important. If someone comes up with a rigorous way to obtain error bars, that's great! I'll take it! My point is that trust should not and will not depend on it. How do you even quantify something like this to a layperson in order to persuade them? Let's do a thought experiment: let's say, we had a self-driving car that's verifiably 10x better than human on average, yet does not provide…

The other poster was talking about error bounds not error bars. That means they want to have a number that tells them how often a system fails and ideally a model of how and when it fails. So they want to have a system that comes with some guarantees. It works X% of the time, fails 100-X% of the time.

Note that X doesn't need to be high, or higher than a human, for example a self-driving car with a 60% failure rate is fine as long as we know when and how it fails. It's this "when and how" that's completely missing from self driving cars.

And in machine learning in general we just don't have any guarantees that a model trained on some dataset will perform the same way in the real world.

Re: Open Problems in Robotics

#98

Post by Mvandenbergh »

Autowired wrote: post #96
Animats wrote: post #66

- Motion planning: already discussed. - Multiaxis singularities: much less of a problem than it used to be. We don't need closed-form solutions any more; we have enough CPU power at the robot to deal with this. You need some additional constraint, like "minimize jerk" when you have too many degrees of freedom. - Simultaneous Location and Mapping. SLAM for short: Getting much better. Things which explore and return a…

Do you think depth estimation could be done with two cameras plus computer vision (to find markers)? I think this is more or less what we do with our own eyes. Of course you would need much more processing power, but maybe for some applications the robot's brain doesn't need to be inside its body.

Parallax methods are widely used, I think it's how Tesla's driving assist features work.

The problem with it is that humans don't just use binocular vision - we have a whole model of the world. So for instance, if I see an object, I usually know roughly how big it is supposed to be because I have a conception of "object". I also know that the straight line on both sides of an object is a wall, and that the wall continues behind the object and therefore the object is in front of the wall and the object is closer than the wall.

That means I'm not just working with my binocular vision and it can be deceptive to think that because it works for human vision it will also work well for computer vision.

Re: Open Problems in Robotics

#99

Post by wazoox »

What I notice from this thread (and many similar ones) is that actually AI may replace sooner or later lawyers, some programmers, system administrators, engineers of various ilks; but the sweeper, the toilet cleaner, the janitor, the restaurant maid and the dishwasher, the delivery guy and the auto repair man, all of the relatively "low skilled" jobs are absolutely safe.

Re: Open Problems in Robotics

#100

Post by [deleted] »

azernik wrote: post #94
mikro2nd wrote: post #59

Earlier quoted context omitted.

Depends what you're after, I guess. If all you want is a 'slave' that does some repeatable action with specified accuracy, then an automation system is just the thing. If you're after exploring the questions around what it means to be an autonomous, sentient being, then perhaps not so much and you really want to build a robot.

The money (and hence most of the engineering time) is in making people's lives and jobs easier, not in exploring deep philosophical questions.

[deleted]
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