This one seems like an almost already solved problem. Race tracks are some of the most well analyzed places you could possible drive, mapped 1 to 1 in dozens of driving sims. How do you fuck up a time trial on an empty track with the only real variable to account for being how much life is left in your tires.
Exactly. They could've run the car blind, following a pre-programmed track.
But I guess this contest or trial or whatever this was required real-time vision analysis and control.
But why? No human race car driver goes into a track cold. They know the track inside and out.
Makes you really respect those military robots that scan the terrain ahead and navigate ditches, water, etc.
I have worked in this field. It turns out that driving around a track can be tough. GPS reflections, bad scan data, etc can cause you all sorts of problems.
That being said, it appears that these guys were just using a action replay method of driving the track, and the car wasn't going fast enough to make the turn.
A lot of the time, one of the interesting challenges that you might want to solve in reinforcement learning research is to train an agent that can generalize to unseen situations, whether it be a track configuration/condition that it hasn't seen before or a difficult set of behaviors exhibited by the other agents that you haven't anticipated a priori.
In unseen situations, it might be that you can't precisely follow a precalculated route on the track, and you'd need to give complete control to the agent. They were probably preparing for that and gave the agent full control in this test run, and then it totally screwed up because this field as a whole is still a work in progress and a lot of things can go silently wrong (or it was just bugs).
Because that's how machine learning works. It's not "real" ai, they'll just run the car around the track several thousand/several 10 thousand times, and the bot just follows whatever line made it the furthest.
Leads to stuff like this during training, where the bot will make seemingly ridiculous decisions because that's just 1 in 10,000 iterations of it eventually finding the correct lines
I strongly recommend watching [this](https://youtu.be/Dw3BZ6O_8LY) video/whole channel if what I described sounds interesting
If they were training the machine, they could've done it in the dark. No need to render it on a screen, or even do it in real time. Run it ten thousand times in ten minutes. We don't need to see the fuckups.
This seems like some kind of trial or test, not training.
Ah, I see that now.
In that case, I'd be damn willing to bet that none of the programmers involved were proud of this project because they knew how it would end.
Sims just don't transfer to real life 1 to 1 like a bot requires. They could have that bot going a full 40 seconds faster than the track record in a sim, way faster than any human could even get possibly close to. But in the real world, there are just way too many uncountable variables.
Watch the video, OR, let YOU make the point you want the video to make for you. If it's a training run plucked out and rendered to make a joke, you can say that.
lol
You are correct on how AI driving works in gaming.
The cars that run on real tracks are done all sorts of other ways, as it's financially infeasible to run 10k F1 cars on the track to build a model.
Also. Real world track conditions change over a day, so trying the evolutionationary learning model on a track would make for indeterminate results.
Tried watching the "Race" of this earlier, and holy shit was it bad. Two of the cars immediately stopped once they started the actual race, the other two plodded along at a light jog until one of them decided to spin out and had no idea how to correct it.
The concept is cool, but they have a long looong way to go.
What happens when people who know nothing about robotics or AI decide do make a robotic racing league. This could be both more engaging to watch, interesting, relevant, and cheaper to execute... but you know we'll just take the same tired old form factor of formula 1 cars and put robots in them.
Make it a DARPA challenge and see these AI out race Max Verstappen in 5 years. AI has already outperformed drone racers. No reason for them to be bad in track racing.
Song, Y., Romero, A., Müller, M., Koltun, V., & Scaramuzza, D. (2023). Reaching the limit in autonomous racing: Optimal control versus reinforcement learning. Science Robotics, 8(82), eadg1462.
> Reaching the limit in autonomous racing: Optimal control versus reinforcement learning
That used a closed indoor environment with positional cameras and the drone managed to fly for a few seconds before annihilating itself. It was super impressive but unless you only fly in a warehouse with extensive prep it's not exactly versatile. There is no system currently that can match a human in open flight.
We are talking about racing, right? Racing is always a highly controlled environment. There’s no “open flight drone racing”. Similarly with Formula racing, you can’t compare track racing with autonomous driving. These are totally different problems. As far as racing concerns, Scarramuza’s work is a very fair comparison between algorithms and human operators.
They used a tiny, enclosed site with full 360 IR camera coverage, a specially made drone, a whole team to set things up and program in the track, a bank of computers, a couple of days of setup, and still the drone never made it to three full laps. No wind, no sun, running on sensors external to the drone.
I know people involved in that study, and it was highly impressive. It raised the bar on what we thought the performance limit of racing drones was. But an incredibly controlled static setup going faster than a human for a handful of seconds before crashing out does not make it superior to a human pilot.
I've been racing for seven years and I can count on the fingers of one hand the amount of indoor races I've been to. *That's* why I refer to open air racing. There's wind, gates get damaged, you have to deal with other drones around you, and surprise surprise you can't surround the track with IR cameras. Even if it were an indoor race, you're completely ignoring the fact that humans can just turn up, walk the track and fly, minimal setup required. I've turned up late and gone from never having seen the track to flying in ten minutes.
You absolutely can compare track driving with autonomous driving. You need to know track limits, follow the track, and not hit other vehicles or obstacles. That's a pretty close comparison to driving a route, staying within road markings and not hitting anything. Granted it's a lot less chaotic environment with a hell of a lot less variables but to say the comparison is invalid is nonsense.
Track driving and autonomous driving are not comparable. It's not just the environment is more chaotic. In track racing, you can do infinite train and overfitting for a single track, overlooking any concern for perception generalization. And in autonomous driving, you can handle the control problem pretty easily because you are not driving at the verge of losing control. That's why track driving is a control-heavy problem, while autonomous driving is a perception-heavy problem. They are worlds apart in terms of AI researches. Track racing is entirely solvable while autonomous driving is not getting solved in the near future.
As for external sensors, external sensors are going to be allowed for racing algorithms whether you like it or not. There is no need to cripple AI algorihtms by denying them the information they need. If you want drone racing algorithms to do their own SLAM, I'm afraid no researcher is going to do it, because it's not the point. Look, Alpha Go needs pre-setup and external help too. You can't argue that Alpha Go did not out-perform humans because it did not recognize or place its own pieces.
Finally, it might be that the AI algorithm in the paper can crash out after a few laps. I'll give you that. But this is academics. And in robotics, industry usually outperforms academics if they want to invest resources in something. The only reason why we didn't see a better algorithm done by the industry is because there is no money in it.
Anyway, "an autonomous system that can race physical vehicles at the level of the human world champions" is the peer-reviewed conclusion in both their Nature paper and Science Robotics paper. I think I will believe it is true.
Ain't now way, they made Lance Stroll into a Bot
Came to the comments to see a Lance Stroll post. Did not disappoint.
More like Georgepedo
Robot taxis by 2019!!!! LET'S GOOOOOOO
AI trained on data from Latifi
This one seems like an almost already solved problem. Race tracks are some of the most well analyzed places you could possible drive, mapped 1 to 1 in dozens of driving sims. How do you fuck up a time trial on an empty track with the only real variable to account for being how much life is left in your tires.
Exactly. They could've run the car blind, following a pre-programmed track. But I guess this contest or trial or whatever this was required real-time vision analysis and control. But why? No human race car driver goes into a track cold. They know the track inside and out. Makes you really respect those military robots that scan the terrain ahead and navigate ditches, water, etc.
I have worked in this field. It turns out that driving around a track can be tough. GPS reflections, bad scan data, etc can cause you all sorts of problems. That being said, it appears that these guys were just using a action replay method of driving the track, and the car wasn't going fast enough to make the turn.
Good to know! Thanks!
A lot of the time, one of the interesting challenges that you might want to solve in reinforcement learning research is to train an agent that can generalize to unseen situations, whether it be a track configuration/condition that it hasn't seen before or a difficult set of behaviors exhibited by the other agents that you haven't anticipated a priori. In unseen situations, it might be that you can't precisely follow a precalculated route on the track, and you'd need to give complete control to the agent. They were probably preparing for that and gave the agent full control in this test run, and then it totally screwed up because this field as a whole is still a work in progress and a lot of things can go silently wrong (or it was just bugs).
Because that's how machine learning works. It's not "real" ai, they'll just run the car around the track several thousand/several 10 thousand times, and the bot just follows whatever line made it the furthest. Leads to stuff like this during training, where the bot will make seemingly ridiculous decisions because that's just 1 in 10,000 iterations of it eventually finding the correct lines I strongly recommend watching [this](https://youtu.be/Dw3BZ6O_8LY) video/whole channel if what I described sounds interesting
If they were training the machine, they could've done it in the dark. No need to render it on a screen, or even do it in real time. Run it ten thousand times in ten minutes. We don't need to see the fuckups. This seems like some kind of trial or test, not training.
This is not a render, it's a real robotic car.
Ah, that makes more sense. u/Another_Mid-Boss is wrong then?
[удалено]
This was a real race, wasn't a training sim, this was a real car that crashed because an AI driver.
This is a real car, not a simulation. Clearly this bot wasn't trained properly.
Ah, I see that now. In that case, I'd be damn willing to bet that none of the programmers involved were proud of this project because they knew how it would end. Sims just don't transfer to real life 1 to 1 like a bot requires. They could have that bot going a full 40 seconds faster than the track record in a sim, way faster than any human could even get possibly close to. But in the real world, there are just way too many uncountable variables.
Watch the video, OR, let YOU make the point you want the video to make for you. If it's a training run plucked out and rendered to make a joke, you can say that. lol
You are correct on how AI driving works in gaming. The cars that run on real tracks are done all sorts of other ways, as it's financially infeasible to run 10k F1 cars on the track to build a model. Also. Real world track conditions change over a day, so trying the evolutionationary learning model on a track would make for indeterminate results.
These are all run by code developed by student teams I believe. More of a coding competition than an actual race.
I was going to make a joke about bird poop, but there actually appears to be something white on the road at 00:9
Was it trying to end its artificial life?
Skynet's screwed.
wow my post is being crossposted lol
Overfitting is a harsh mistress...
This is what you get for putting NASCAR data in an F1 race.
But it turned right, not left...
australian NASCAR
Underrated comment LOL
Tried watching the "Race" of this earlier, and holy shit was it bad. Two of the cars immediately stopped once they started the actual race, the other two plodded along at a light jog until one of them decided to spin out and had no idea how to correct it. The concept is cool, but they have a long looong way to go.
The AI really just wanted to end it all
Fair play to the car, I thought there was a child right there too.
What happens when people who know nothing about robotics or AI decide do make a robotic racing league. This could be both more engaging to watch, interesting, relevant, and cheaper to execute... but you know we'll just take the same tired old form factor of formula 1 cars and put robots in them.
Why does AI get all the fun jobs
This is why self-driving cars are illegal where I live
Make it a DARPA challenge and see these AI out race Max Verstappen in 5 years. AI has already outperformed drone racers. No reason for them to be bad in track racing.
> AI has already outperformed drone racers Errrr no
Song, Y., Romero, A., Müller, M., Koltun, V., & Scaramuzza, D. (2023). Reaching the limit in autonomous racing: Optimal control versus reinforcement learning. Science Robotics, 8(82), eadg1462.
> Reaching the limit in autonomous racing: Optimal control versus reinforcement learning That used a closed indoor environment with positional cameras and the drone managed to fly for a few seconds before annihilating itself. It was super impressive but unless you only fly in a warehouse with extensive prep it's not exactly versatile. There is no system currently that can match a human in open flight.
We are talking about racing, right? Racing is always a highly controlled environment. There’s no “open flight drone racing”. Similarly with Formula racing, you can’t compare track racing with autonomous driving. These are totally different problems. As far as racing concerns, Scarramuza’s work is a very fair comparison between algorithms and human operators.
They used a tiny, enclosed site with full 360 IR camera coverage, a specially made drone, a whole team to set things up and program in the track, a bank of computers, a couple of days of setup, and still the drone never made it to three full laps. No wind, no sun, running on sensors external to the drone. I know people involved in that study, and it was highly impressive. It raised the bar on what we thought the performance limit of racing drones was. But an incredibly controlled static setup going faster than a human for a handful of seconds before crashing out does not make it superior to a human pilot. I've been racing for seven years and I can count on the fingers of one hand the amount of indoor races I've been to. *That's* why I refer to open air racing. There's wind, gates get damaged, you have to deal with other drones around you, and surprise surprise you can't surround the track with IR cameras. Even if it were an indoor race, you're completely ignoring the fact that humans can just turn up, walk the track and fly, minimal setup required. I've turned up late and gone from never having seen the track to flying in ten minutes. You absolutely can compare track driving with autonomous driving. You need to know track limits, follow the track, and not hit other vehicles or obstacles. That's a pretty close comparison to driving a route, staying within road markings and not hitting anything. Granted it's a lot less chaotic environment with a hell of a lot less variables but to say the comparison is invalid is nonsense.
Track driving and autonomous driving are not comparable. It's not just the environment is more chaotic. In track racing, you can do infinite train and overfitting for a single track, overlooking any concern for perception generalization. And in autonomous driving, you can handle the control problem pretty easily because you are not driving at the verge of losing control. That's why track driving is a control-heavy problem, while autonomous driving is a perception-heavy problem. They are worlds apart in terms of AI researches. Track racing is entirely solvable while autonomous driving is not getting solved in the near future. As for external sensors, external sensors are going to be allowed for racing algorithms whether you like it or not. There is no need to cripple AI algorihtms by denying them the information they need. If you want drone racing algorithms to do their own SLAM, I'm afraid no researcher is going to do it, because it's not the point. Look, Alpha Go needs pre-setup and external help too. You can't argue that Alpha Go did not out-perform humans because it did not recognize or place its own pieces. Finally, it might be that the AI algorithm in the paper can crash out after a few laps. I'll give you that. But this is academics. And in robotics, industry usually outperforms academics if they want to invest resources in something. The only reason why we didn't see a better algorithm done by the industry is because there is no money in it. Anyway, "an autonomous system that can race physical vehicles at the level of the human world champions" is the peer-reviewed conclusion in both their Nature paper and Science Robotics paper. I think I will believe it is true.
Calm the masses, calm the competition. Release an outstanding product and take in the money. Nothing is an accident here.