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Open Problems in Robotics

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Re: Open Problems in Robotics

#31

Post by imtringued »

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?

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.

Re: Open Problems in Robotics

#32

Post by cpgxiii »

fxtentacle wrote: post #24
alwahi wrote: post #19

Earlier quoted context omitted.

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.

It's generally understood that motion planning for nontrivial problems is very non-convex, and it's basically impossible to predict if a given initial condition can be optimized to the global optima. Even simple convex 3D geometry in the environment projects to very complex non-convex geometry in the configuration space of the robot (the space you're performing optimization in).

Re: Open Problems in Robotics

#33

Post by tuatoru »

mvn9 wrote: post #17
monkeydust wrote: post #5

Honestly, late yesterday evening after work I was looking at floor full of toys that my little kids were playing with in yet another lockdown day thinking I wish there was a robot that I could build or buy to tidy this up. Did some research found this research project, promising but from 2018 and looks like it didn't go anywhere. https://youtu.be/geub-Nuu-Vw So now I'm thinking what about the build option.

Build it. There is a global market of parents who will buy it. But on the other hand, why not accept the toys on the floor? You are fighting entropy for no reason. You sleep at night, you will work tomorrow during the day, and when you look again, the toys are in an equally dispersed state. Why not let them stay in that state for days until you need to hover?

> Why not let them stay in that state for days until you need to ho[o]ver?

Risk of personal injury. (The Lego-on-the-stairs scenario.)

Also, some people just like a calm visual field at home.

Re: Open Problems in Robotics

#34

Post by imtringued »

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 have a mental model of humans, they can predict what another human is going to do with reasonable accuracy. Robots are wholly artificial so there is no existing mental model we can apply to them to understand why and how they act.

Re: Open Problems in Robotics

#35

Post by kajecounterhack »

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…

> what actually does convex mean in the context of optimization

It means there's a deterministic solution.

Longer explanation:

When you're optimizing a function in math, the function's solutions exist in N-dimensional space. If the space of solutions is a hyperplane in this space, and you want to find a value on that plane that minimizes the function, a convex plane will allow you to find the minimum every time, deterministically -- one way is to simply follow the negative gradient of the plane. A non-convex plane may have lots of holes or other shapes (e.g "saddle points"), so if you follow the negative gradient, you'll end up thinking you're at a minimum, but turns out you're only at a _local_ minimum and there may be another, better solution out there.

So whenever you see "convex" read: a best solution exists. Whenever you see "non-convex" read: an approximation is your best hope. Motion planning is non-convex.

From https://en.wikipedia.org/wiki/Convex_function: "Any local minimum of a convex function is also a global minimum. A strictly convex function will have at most one global minimum.[4]"

Re: Open Problems in Robotics

#36

Post by imtringued »

Shared404 wrote: post #21
m0zg wrote: post #14

Earlier quoted context omitted.

> 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.

Trust is a continuum. I don't see how you can say that you do not trust humans at least to some extent, no matter how tiny that sliver of trust is.

Re: Open Problems in Robotics

#37

Post by snovv_crash »

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…

I discovered when doing something in this space recently that even the simple case of doing optimal planning for a particle under Newtonian dynamics, with limits on eg. velocity and acceleration, is NP-Complete.

Re: Open Problems in Robotics

#38

Post by fxtentacle »

Motion planning is also an Open Problem for Humans!

Let's say we are standing on a high hill and I point to another hill and say: "Walk over there". Do you expect any human to find a reasonably good path by themselves? I would personally try to use a map. How do military robots solve this? They use satellite images.

And in general, this article seems very pessimistic to me. My home-built computer vision pipeline can do localization and mapping, loop detection, object segmentation and depth estimation at levels that are "good enough" for indoor drone flight. So I would assume that someone with a generous serving of financial resources would be able to solve most of those problems, except for 2 issues:

1. You need to memorize how the environment works. That's why newborn kids take stupid decisions, they lack the stochastic priors. Lucky for us, memorizing is AIs strong point.

2. You need to have mechanics that are more forgiving. If I accidentally position my hand the wrong way, I might accidentally squeeze someone a bit, but I won't crush their bones, because the mechanics of my arm are flexible. We'll need better accentuators and elastic casings.

And just for the sake of discussion, here's my replies for each problem category.

Simultaneous Location and Mapping: There are libraries that work well enough for your robot to localize itself in a building-sized environment with just a single camera. I'd consider this solved. https://github.com/raulmur/ORB_SLAM2

As for the obstacles, also for humans it is mostly guessing if you want to step on that blanket or if there'll be something fragile or slippery inside.

Lost Robot Problem: Most SLAM solutions are good enough that you could just regenerate the map from scratch every time that there was a gap in your perception. ORB-SLAM2 also has a loop and merge detection module so that if you reset its tracking and then it walks into a known environment, it can merge the old data into its new state.

Depth estimation: It works well enough in practice. https://www.stereolabs.com/zed-2/

Scene understanding: I don't know about you, but when I drive the highway, I sometimes have dead flies on my windshield. Apparently, they aren't that clever after all.

Position estimation: It works exceptionally well for VR markers. In general, those solutions tend to be called "Visual Odometry" https://www.youtube.com/watch?v=fh5dLF3dmr0

Affordance discovery: This is mostly a memorization problem, so a perfect candidate for AI.

Re: Open Problems in Robotics

#39

Post by [deleted] »

snovv_crash wrote: post #37
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…

I discovered when doing something in this space recently that even the simple case of doing optimal planning for a particle under Newtonian dynamics, with limits on eg. velocity and acceleration, is NP-Complete.

[deleted]

Re: Open Problems in Robotics

#40

Post by mvn9 »

tuatoru wrote: post #33
mvn9 wrote: post #17

Earlier quoted context omitted.

Build it. There is a global market of parents who will buy it. But on the other hand, why not accept the toys on the floor? You are fighting entropy for no reason. You sleep at night, you will work tomorrow during the day, and when you look again, the toys are in an equally dispersed state. Why not let them stay in that state for days until you need to hover?

> Why not let them stay in that state for days until you need to ho[o]ver? Risk of personal injury. (The Lego-on-the-stairs scenario.) Also, some people just like a calm visual field at home.

All very reasonable arguments. What I don't understand is cleaning up in the evening. There is no visual field to perceive if you are asleep.
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