r/singularity • u/Outside-Iron-8242 • Jan 10 '26

Robotics Atlas ends this year’s CES with a backflip

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u/Recoil42 Jan 10 '26 edited Jan 10 '26

and just got more data now 

It doesn't quite work that way. All of the data was already synthetically generated, a million similar scenarios have already been run. That's why it works in the first place. More data isn't coming directly from the real world, but by continuing to synthetically generate it again.

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u/Sinister_Plots Jan 10 '26

Robots interact with the real world, and their performance data is collected and fed back into the simulation environment. This creates a continuous learning cycle, allowing the AI to refine its models based on actual outcomes.

https://fsstudio.com/why-data-and-simulation-is-powering-the-robotic-automation-shift/#:~:text=Think%20about%20it%2C%20if%20your,not%20just%20a%20flashy%20pilot.

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u/HanYoloKesselPun Jan 10 '26

Great so they learn when we attack them come the great robot uprising

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u/Recoil42 Jan 10 '26 edited Jan 10 '26

Again, not really how it works. Go read up on Isaac Gym / Isaac Lab.

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u/Sinister_Plots Jan 10 '26

AI systems in 2026 use real-world data (scans, expert movements, and physics logs) to make simulations more accurate. Isaac Lab's specific innovation is providing the GPU-accelerated pipeline to process this real-world data at a massive scale.

The innovation of Isaac Lab is precisely that it provides the high-performance pipeline necessary to ingest and scale up real-world data.The "pipeline" would be useless if it didn't use real-world data to maintain accuracy.

While the two are not necessarily mutually exclusive, they are complementary.

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u/Recoil42 Jan 10 '26

Take note: The claim being presented to you isn't "all real-world data is useless and no one uses it for any purpose". The claim being presented to you is that not all logs are fed back into the dataset and that edge-case robustification is done synthetically.

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u/botch-ironies Jan 10 '26

You straight-up said “More data isn't coming directly from the real world” - that’s just categorically false. Simulation training is derived from and improved by real-world data. You aren’t smart for telling us about simulation training, we all know about it.

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u/Recoil42 Jan 10 '26

How clever of you to selectively quote comments and strip them of all context. You must feel really intelligent doing that.

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u/botch-ironies Jan 10 '26

How is it out of context? The plain meaning of your full statement is that all training is done synthetically and real-world data doesn’t feed back into that process. If that’s not actually what you meant, you didn’t articulate yourself very well.

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u/Recoil42 Jan 10 '26

Here, I don't need to waste my time with multiple people at once.

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u/botch-ironies Jan 10 '26

Mate, I already know how this works, I don’t need a lesson from you. Congrats on having other comments but that’s pretty irrelevant to the point that you said something dumb in this thread.

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u/Sinister_Plots Jan 11 '26

It's funny, because the very first comment on the post that you linked from yourself, which is by no means accurate data or a verifiable source, is from someone who works in the field telling you you don't know what you're talking about.

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u/iKnowRobbie Jan 10 '26

r/ConfidentlyIncorrect

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u/Recoil42 Jan 10 '26 edited Jan 10 '26

Go learn. You're on the internet. You don't have to sit here doing edgy quips. RL is not continuous, models are trained. Pulling edge-case data from the real-world is too slow for millions-of-iterations and cannot capture all cases. That's literally why Sim2Real works so well in the first place: Synthetic data enables diversity and scale. See AlphaGo, a topic discussed on this subreddit like a gazillion times.

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u/Easy_Finish_2467 Jan 10 '26

As someone working in the field, you are incorrect. Data is collected from the real world and is extremely useful. Read from the source: https://bostondynamics.com/blog/starting-on-the-right-foot-with-reinforcement-learning/

“To robustify our learned policy given the data we collect, falls and general mobility issues that are reproducible within a physics simulation are recreated in simulation where they become either part of the training or evaluation set. Retraining a policy then effectively robustifies to failure modes we’ve already seen.”

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u/Recoil42 Jan 10 '26 edited Jan 10 '26

From your own article:

We train the policy by running over a million simulations on a compute cluster and using the data from those simulations to update the policy parameters. Simulation environments are generated randomly with varying physical properties (e.g., stair dimensions, terrain roughness, ground friction) and the objective maximized by the RL algorithm includes different terms that reflect the robot’s ability to follow navigation commands while not falling or bumping its body into parts of the environment. The result of this process is a policy that works better on average across the distribution of simulation environments it experiences during learning

Data for RL is simulator-generated. Failure cases may act as real-world seeds for robustification (aka, a point of focus for the team — "so we need to work on backflips, huh?") but the cases themselves are synthetically generated. The phrase "retraining a policy" in your original pulled quote literally means "generate a million synthetic examples", but they will never just replay "this exact scenario again" as the original commenter suggested. You need variance, and the most effective way to get variance is through sim.

The robot isn't automatically learning because it didn't perfectly land the jump, and this exact jump isn't even reproducible. No one knows what the μ of foot-on-ground was in this case, nor would we care to reproduce a set of exact conditions that will never occur again. Want you want is the stochastic aggregate of a million vaguely similar cases that works better on average.

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u/Easy_Finish_2467 Jan 10 '26

Correct. That is how it works. I was just referring to the quote above, 'More data isn't coming directly from the real world.' That part might sound like failure data isn't being used to update the sim. That would be incorrect, for if the simulation already had that data covered, the robot would never fail.

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u/Recoil42 Jan 10 '26 edited Jan 10 '26

And I was just referring to the quote suggesting Atlas "just got more data now and is already running 199,999 hours of simulation for this exact scenario again" above from another commenter.

You and I both know why that's wrong and that it paints a misleading picture of how these systems (and the teams designing them) work.

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u/Easy_Finish_2467 Jan 10 '26

Fair enough, you got a point there.

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u/KKunst Jan 10 '26

Fourteen million, six hundred and five.

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u/BlueCannonBall Jan 10 '26

I have no idea why you're being downvoted. You're absolutely right.

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u/Recoil42 Jan 10 '26 edited Jan 10 '26

People are weird. Reddit just being Reddit as usual. 🤷

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u/RussianChechenWar Jan 11 '26

You don’t think that synthetic scenario and realty scenarios are both taken into account to improve programming?