41–50 of 232 posts
Is there a similar report for open problems in AI or ML?
"Hey Siri, write up minutes of the meeting we just had and email them to everybody".
There are quite a few open problems in that, including analogous navigation of an ill-defined environment which nevertheless has regularities.
Earlier quoted context omitted.
> 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.
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…
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 distance doesn't work. But 1cm precision at 50cm distance is doable. And for a robot arm, you mainly need the precision when you're close to the object.
And yes, I am talking about what you probably meant with learned stereo matching. I would call it close to solved because we can by now do unsupervised training and achieve usable results. While I had trouble reproducing this specific paper, the general idea is valid: https://github.com/google-research/google-research/tree/mast... https://arxiv.org/abs/1904.04998
We are also seeing good results from using random YouTube videos to train AI vision.
But given that you are so sure that this is unsolved, I wonder if I should start a company to sell my depth estimate pipeline. Would you have any example image pairs that are causing problems, so that I can see visually what fails?
Earlier quoted context omitted.
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).
Earlier quoted context omitted.
> 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.
The objections to neural nets as the solution to all the problems on his list are the same as the old objections to neural nets as the solution to computer vision, speech recognition, translation, playing Go, etc. The objections will fall in the face of overwhelming evidence that neural nets simply work better than other approaches to these types of problems. For a long time software was the reason robots didn't work…
Perhaps? But progress is stalling[1]. We need theoretical foundations.
[1] https://www.sciencemag.org/news/2020/05/eye-catching-advance...
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…
Usually mathematics and (proper) algorithms is the topic where everything is 100% (good quality actually finished work, not an early prototype released as final is assumed). It just may or may not to be fully relevant to our life.
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…
Do we need to add kinaesthesia to the list of open problems then?
The objections to neural nets as the solution to all the problems on his list are the same as the old objections to neural nets as the solution to computer vision, speech recognition, translation, playing Go, etc. The objections will fall in the face of overwhelming evidence that neural nets simply work better than other approaches to these types of problems. For a long time software was the reason robots didn't work…