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Open Problems in Robotics

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Re: Open Problems in Robotics

#101

Post by helltone »

mike_mg wrote: post #22
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 propert…

It seems you are taking about theoretical error bounds, that is proofs in papers with assumptions on input probabilities etc. These don't always apply to actual implementations, in physical real robots. There is a huge gap in safety between practices in aerospace engineering and robotics.

Re: Open Problems in Robotics

#102

Post by fxtentacle »

cpgxiii wrote: post #62
fxtentacle wrote: post #45

Earlier quoted context omitted.

While that is true, the critical question is whether it will be locally smooth once you get close enough to your goal. Long-distance planning is generally bad, even for humans. Short-distance planning tends to work well with A-star

The problem is that "distance" here is not necessarily in any intuitive or useful space, and knowing if you are close to the goal may be as hard as finding the full solution. You can be quite "close" to a solution in, say, Euclidean distance over joint angles, while constraints like joint limits and collisions mean that the actual solution path will be quite long.

I apologize, I hadn't thought about robot arms. All the planning stuff that I was involved with so far was either quadcopters or slowfliers or buggies, so only cases where the Euclidean norm is useful.

Re: Open Problems in Robotics

#103

Post by roland35 »

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…

Thank you for putting what I have always thought into a clear paragraph!

Back when I was working on a consumer robotics system that issue (what can we expect a random consumer to do to their home/yard to simplify the robot's job) was a constant tension with marketing. Because obviously they want a product that a customer just drops into their yard and does everything including weeding and picking up dog poop with no setup.

However, if we can help the robot by modifying the environment a little, it makes a huge improvement to reliability and cost. I would love to work on robots again, it is a fun problem to try and work out!

Re: Open Problems in Robotics

#104

Post by michaelt »

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.

The answer is 'sometimes' and it's known as stereo vision.

Downsides include being less precise at longer distances (an object 1.5 meters away becomes a lot larger if it gets 1 meter closer, an object 150 meters away barely changes) and poor performance on surfaces with fewer features, or really dense features that all look alike (e.g. running across a field while spotting bumps and dips in the grass, or measuring whether sheets of steel are flat or not)

In some cases this doesn't matter - a Roomba doesn't care if it's hard to see things 150 meters away, as rooms are rarely that big.

Re: Open Problems in Robotics

#105

Post by treeman79 »

Mvandenbergh wrote: post #98
Autowired wrote: post #96

Earlier quoted context omitted.

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 conti…

Lost binocular vision for a number of years back.

You get around just fine without it. Got it back after some speciality glasses (PRISM).

Was a complete shock see depth again. Didn’t seem to help anything getting it back. Mostly just trippy. Chairs were amazing to stare at.

Re: Open Problems in Robotics

#106

Post by fxtentacle »

rimliu wrote: post #68
fxtentacle wrote: post #38

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…

Alas, your problem #1 is not really about memorization, it is about understanding. Take a human to the house they have never been before and tell them: "make me some tea". Now try that with any robot you want. It is you being optimistic, not the article being optimistic.

Give a human kid a piece of paper and an envelope and say "make it fit" and roughly 1 out of 5 will fail because they have not yet had the opportunity to watch their parents fold a letter.

For your example, I see an upfront memorization component, which is that your request only works with humans that have previously seen how tea is made. That would be an unsuperwised AI which watches a youtube tutorial and then reduces the task to "get hot water + get tea bag + get container + combine"

Please note how by cultural memorization is again implicitly added. I might use a trash can as the container, but due to our shared culture we'll agree that a mug works best. So this gets reduced with more unsupervised AI to resolve "container" to a list of tolerable objects.

Next comes an exploration phase where human and robot just randomly open cupboards to see what is inside. YOLO should be good enough to recognize the water cooker, the tea bags, and the mug.

Next up comes again memorization. Kids cannot reliably turn on a machine that they have not seen before, so intelligence is probably of little use. Instead, they learn by imitating. An AI would probably again crawl random YouTube videos, check that the cooker looks similarly, then try to imitate that.

I hope I have illustrated that a lot of what we think of as understanding is not much more than repeating a similar situation which we have previously experienced.

That would also be my theory as to why meditating and thinking about an action can actually improve our skill at doing it. We're memorizing a fantasy simulation.

Re: Open Problems in Robotics

#107

Post by fxtentacle »

mvn9 wrote: post #54
fxtentacle wrote: post #46

Earlier quoted context omitted.

It's to make sure that when you walk around half-asleep early in the morning trying to get a diaper, that you don't step on a pointy lego brick.

Do you walk heel or toe first [1]? I would assume that lego bricks only hurt when moving heel first since humans had to deal with stony environments for quite some time. Not that you should stop cleaning up but this could be another technology to deal with the bricks. [1] https://news.ycombinator.com/item?id=1086446 Barefoot Running

Heel first. Interesting idea :)

Re: Open Problems in Robotics

#108

Post by thdrdt »

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…

Thinking about car painting robots it's clearly about cost.

You can program a car painting robot in some hours. This will be way cheaper than a robot that is aware of the car and knows how it should move to perform the best paint job.

Another example is your washing machine. It would be nice to have a washing robot that would sort your clothes and wash them. But it is way cheaper to sort clothes yourself. So a cloth sorting problem might be an open problem but is indeed a nice-to-have.

Re: Open Problems in Robotics

#109

Post by legulere »

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…

With specialized automation you get a lock-in effect because they are still expensive enough that you have to use them when you buy them.

Re: Open Problems in Robotics

#110

Post by legulere »

thdrdt wrote: post #108
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…

Thinking about car painting robots it's clearly about cost. You can program a car painting robot in some hours. This will be way cheaper than a robot that is aware of the car and knows how it should move to perform the best paint job. Another example is your washing machine. It would be nice to have a washing robot that would sort your clothes and wash them. But it is way cheaper to sort clothes yourself. So a cloth…

Another open problem: taking machines apart for recycling.
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