51–60 of 232 posts
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…
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. walking robot that accesses your fridge because cute or novel)?
It seems both the lay population and academia assumes the latter, but industry demands the former.
This is perhaps why there are so many "open problems" - because they're not actually real world problems: they're just academic nice-to-haves.
Earlier quoted context omitted.
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…
I strongly disagree! I have seen fantastic results with stereo cameras, colored lights, and self-calibration. For most use cases, it's no problem if your robot will stop for a few seconds, rotate the camera axis around a bit, and then continue. But that appears to be good enough to calibrate the features for tracking things like a reflective and transparent glass jar. As for the precision, I agree that 1cm at 10m dis…
We have in-house research work on both learned monocular depth (so-so for robotics tasks) and learned stereo disparity (much more promising), so progress here isn't impossible, but none of the off-the-shelf products really solve the problem.
Earlier quoted context omitted.
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.
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.
[1] https://news.ycombinator.com/item?id=1086446 Barefoot Running
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…
Is obtaining optimal solutions a requirement in motion planning for robotics?
This is a math problem applied to robotics, not a unique problem to robotics. If there are workarounds, then isn't the problem solved?> Factories have robotic arms and they seem to do an adequate job making cars and other stuff in spite of singularities.
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…
> I’ll point out that the humble housefly has no problem understanding the concept of “shit in front of you; avoid,” Surely in the case of the house fly, it would be more like 'dinner is served' than 'avoid'! But in all seriousness, this is a good list to remind us how vastly far away from human-level AI we are.
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…
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.
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…
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 "error bars". I know we don't have one now, and unlikely to have one in the foreseeable future, but bear with me here, for the sake of argument.
Would you trust it, rather than a random human Uber driver?
FWIW, I'm amazed that driving cars manually is perceived as normal every time I drive one. I can easily accelerate 2+ metric tons of metal to 100+ MPH, and get distracted, launching this deadly projectile with me inside into oncoming traffic. Most roads have _no dividers_. This does happen from time to time, lots of people die. Nobody gives a shit.
Humans suck so bad at so many things that robots will be better than them at a lot of fairly unconstrained tasks in the next decade or two. And I'm pretty certain they won't have error bars while doing what they do. Humans don't.