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Autonomous DeLorean drives sideways to move forward

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Re: Autonomous DeLorean drives sideways to move forward

#41

Post by Animats »

sillysaurusx wrote: post #35
AndrewKemendo wrote: post #12

Earlier quoted context omitted.

>They use the trained net as a tool for building a controller with known continuity properties. This sentence and comment is a revelation to me so thanks! I was very into control theory from 2001-2006 before jumping into Bayes nets and eventually ML. Having not really returned to CT since, it's always been a question in my mind how to use ML for SCADA & PLCs without running into the problems that you describe. As wit…

I would highly recommend https://www.youtube.com/watch?v=PYylPRX6z4Q as a way of framing the problem. It seems like our options for ML are currently: self-play, dataset fitting, or human feedback. It's possible to solve any problem that can fit into one of those molds. (Unfortunately the opposite is true: if a problem doesn't fit into those categories, it seems very hard to solve with any form of ML.) But, now that I…

One approach is that you have a semi-traditional control system, which has integrators, filters, adders, etc. All these components have tuning parameters, and tuning complex controllers is hard. But once you get them tuned, they behave in a reasonably predictable way.

The machine learning part is used to get a predictor. You operate the thing and train a model of what will happen for various inputs. The machine learning system is just an observer in this training phase; it doesn't control anything. You train from recorded data.

Then you use the trained model to tune the controller. The model lets you get an output for any set of inputs, so you can now choose input test sets which are suitable for tuning the model, like changing one input at a time and noting the output change.

Automatically generating the structure of the controller (how those blocks are connected) is a separate problem. That's called "system identification".

See [1].

[1] http://www.mpc.berkeley.edu/research/adaptive-and-learning-p...

Re: Autonomous DeLorean drives sideways to move forward

#42

Post by metaphor »

chongli wrote: post #18
djmips wrote: post #14

Earlier quoted context omitted.

If you notice in the video there are no nearby hard walls, it's a giant open parking lot and the course is set up by soft pieces like plastic traffic cones. I'm sure a course could be made with appropriate margins for safety. Even so, in the ride world there are accidents but that doesn't stop people from riding.

Not to mention the fact that this system is monitoring the traction of all 4 wheels constantly and would detect the slip in milliseconds, transferring power to other wheels. It would be fine. They probably already have a bunch of lubricant on that lot in order to reduce wear on the tires.

> They probably already have a bunch of lubricant on that lot in order to reduce wear on the tires.

That's a real skid pad at a real road course[1] with real rules and regulations[2], not some expendable engineering testbed greased up to save tires.

[1] https://www.thunderhill.com/renting/skid-pad

[2] https://www.thunderhill.com/s/Skidpad-Event-Guidelines-2018-...

Re: Autonomous DeLorean drives sideways to move forward

#43

Post by allovernow »

Animats wrote: post #6

They did most of that back in 2015.[1] This is version 2. It's interesting what's happening as the control theory people get into machine learning. The controls people don't typically run a neural net as a controller. They use the trained net as a tool for building a controller with known continuity properties. The trouble with pure neural net controllers is that they sometimes do something totally bogus for some dat…

>The trouble with pure neural net controllers is that they sometimes do something totally bogus for some data point within the normal input space. That's not OK in control systems.

Is that actually true? Everything we do at work with neural nets seems to indicate continuity. We do stuff with mathematical simulations and imagelike processing. Latent space for auto encoders tend to be smoothly varying throughout the range of outputs.

I thought the problem is just that it isn't proven that neural nets are sufficiently smooth.

Re: Autonomous DeLorean drives sideways to move forward

#44

Post by allovernow »

charliepark wrote: post #39
Animats wrote: post #6

They did most of that back in 2015.[1] This is version 2. It's interesting what's happening as the control theory people get into machine learning. The controls people don't typically run a neural net as a controller. They use the trained net as a tool for building a controller with known continuity properties. The trouble with pure neural net controllers is that they sometimes do something totally bogus for some dat…

> "The trouble with pure neural net controllers is that they sometimes do something totally bogus for some data point within the normal input space." A friend's theory on Google Maps is that it sometimes intentionally sends you down a non-optimal path in order to gather data on alternate routes. This friend isn't in the ML space, so this is just their anecdotal observation. Are they right, then?

Unlikely. Usually a suboptimal route is chosen when there's traffic somewhere else. Sometimes traffic might clear up after the path is chosen. Its an appropriate theory though, given that this is Google we're talking about.

Re: Autonomous DeLorean drives sideways to move forward

#47

Post by tcoff91 »

sillysaurusx wrote: post #35
AndrewKemendo wrote: post #12

Earlier quoted context omitted.

>They use the trained net as a tool for building a controller with known continuity properties. This sentence and comment is a revelation to me so thanks! I was very into control theory from 2001-2006 before jumping into Bayes nets and eventually ML. Having not really returned to CT since, it's always been a question in my mind how to use ML for SCADA & PLCs without running into the problems that you describe. As wit…

I would highly recommend https://www.youtube.com/watch?v=PYylPRX6z4Q as a way of framing the problem. It seems like our options for ML are currently: self-play, dataset fitting, or human feedback. It's possible to solve any problem that can fit into one of those molds. (Unfortunately the opposite is true: if a problem doesn't fit into those categories, it seems very hard to solve with any form of ML.) But, now that I…

Perhaps it could just try and learn the parameters to some kind of pre existing control theory algorithm? Perhaps with a simulator it could fall into the first category you mentioned.

Re: Autonomous DeLorean drives sideways to move forward

#48

Post by mjburgess »

allovernow wrote: post #43
Animats wrote: post #6

They did most of that back in 2015.[1] This is version 2. It's interesting what's happening as the control theory people get into machine learning. The controls people don't typically run a neural net as a controller. They use the trained net as a tool for building a controller with known continuity properties. The trouble with pure neural net controllers is that they sometimes do something totally bogus for some dat…

>The trouble with pure neural net controllers is that they sometimes do something totally bogus for some data point within the normal input space. That's not OK in control systems. Is that actually true? Everything we do at work with neural nets seems to indicate continuity. We do stuff with mathematical simulations and imagelike processing. Latent space for auto encoders tend to be smoothly varying throughout the ra…

It's not a continuity problem. It's that "bogus" is defined with respect to what it should do, and that criteria requires a working understanding of the environment which isn't present in the model.

If you had all possible images of all possible roads in all possible (lighting, weather,..) conditions, etc. then the ideal behaviour is just a function from all inputs to all desired actions. In the absence of this infinity of data you need a model. Any model which has no explicit understanding of the causal behaviour of an environment is going to misbehave for one of those unseen data points (eg. an unseen road in a weather/lighting/etc. condition).

"Correct"(/Safe) behaviour is not a function of pixels. No statistical model which associates pixel patterns with action can be correct.

Re: Autonomous DeLorean drives sideways to move forward

#49

Post by metaphor »

yobananaboy wrote: post #20
djmips wrote: post #14

Earlier quoted context omitted.

If you notice in the video there are no nearby hard walls, it's a giant open parking lot and the course is set up by soft pieces like plastic traffic cones. I'm sure a course could be made with appropriate margins for safety. Even so, in the ride world there are accidents but that doesn't stop people from riding.

On top of that, there's the deadman's switch on the console that stops the car if the driver releases it. Would definitely pay to ride in something like this.

It's an e-stop button (probably NFPA 79 Cat 0 implementation), not dead man's switch. This timestamp[1] clearly depicts an air gap between fingers and button while maneuvering.

[1] https://youtu.be/3x3SqeSdrAE?t=74

Re: Autonomous DeLorean drives sideways to move forward

#50

Post by pjc50 »

Animats wrote: post #6

They did most of that back in 2015.[1] This is version 2. It's interesting what's happening as the control theory people get into machine learning. The controls people don't typically run a neural net as a controller. They use the trained net as a tool for building a controller with known continuity properties. The trouble with pure neural net controllers is that they sometimes do something totally bogus for some dat…

That was roughly the thesis of Fuzzy Logic (Kosko 1993); complex control systems that used neural nets during design to extract "rules" from the system under control.
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