/AI9h ago

Developer TACIXAT trains a drone flight controller using a compact 3x64 neural network on a single RTX 3090

Yacine claims the same policy trains from pixels in two minutes

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#421Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)
kache
TACIXAT
Sentiment

Positive users praise small neural nets training drone policies from pixels on a single RTX 3090 as a refreshing alternative to large models, while negative users dismiss the approach as unnecessary or inferior to traditional programming.

Pos
55.0%
Neg
45.0%
7 comments with sentiment.
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kache@yacineMTB

I can train the same policy in 2 minutes, policies that are trained off of pixels

TACIXAT@TACIXAT

idk you can train drones to fly on a 3090 in a couple of hours, this is a 3x64 nn

7hViews 13.5KLikes 163Bookmarks 60
TACIXAT@TACIXAT

@NigelHiggs7 TU Delft has some research on this, training with domain randomization on the drone configs allows it to transition to a real drone pretty well https://arxiv.org/pdf/2504.21586

that's what I'm working on now for AI Grand Prix, first my sim to their sim, then to real for the finals

7hViews 1.6KLikes 30Bookmarks 16
TACIXAT@TACIXAT

@mSanterre I wrote it from scratch, JAX for drone kinematics and Raylib Python bindings for visualization

7hViews 1.3KLikes 46Bookmarks 10
kache@yacineMTB

just FYI

7hViews 2.5KLikes 19Bookmarks 2
kache@yacineMTB

That also pass sim2real

7hViews 2.7KLikes 19Bookmarks 1
Nigel Higgs@NigelHiggs7

@TACIXAT That’s sick although, how well does this generalize to a real drone situation?

7hViews 1.8KLikes 2
Henry Paradiz@0xtechnexus

@TACIXAT yea but would you strap a 3090 to a drone and blow it up

9hViews 627Likes 1
max@mSanterre

@TACIXAT Damn son that's cracked

7hViews 387Likes 10
Nicholas Perry@RegolithHunter

@TACIXAT And you can also make custom modules that can give you significant speed increases. I hit 9100x compute speedup just by prompting an optimized module.

Creativity can often beat intelligence.

7hViews 797Likes 1Bookmarks 1
max@mSanterre

@TACIXAT Which sim is this?

7hViews 1.4KLikes 3
Ashish kumar Singh@ashishkmr472

@TACIXAT Looks great, what algorithm did you use? I built something similar on my 4080 too, pure fpv mode with just stick controls - https://github.com/AshishKumar4/FlyDreamer

4hViews 179Likes 4
Rev. Poppy Haze 𓅋@poppy_haze

@TACIXAT the important bit is to get it to run on a ruggedized Intel Atom, and CF card that costs the taxpayer $200 each

6hViews 126Likes 4
Muh Tweeter@muh_tweeter

@NigelHiggs7 @TACIXAT this is also a pretty interesting paper for positional controllers https://arxiv.org/html/2509.11481v1 I believe it's even upstreamed into px4 now if you build it correctly

6hViews 19Likes 1Bookmarks 1
name@exigenced

@TACIXAT Their compute constraint is coming from inside the house

7hViews 438Likes 5
Azide@evilmoderazide

@TACIXAT i hate how "useful experimentation" now usually just means llms

7hViews 477Likes 4
N@Mirim08166421

@TACIXAT Do you think this has any advantage over algorithmically maneuvering the drone?

5hViews 306
JS@spoozpriest

@TACIXAT also like the gymnasium framework is 100% right there and it accomplishes insane shit off a 10 year old mac cpu.

7hViews 544Likes 3
CommonKnowledge@KnowCommon

@0xtechnexus @TACIXAT You train on a 3090 and quantize the model so that it can run on a smartphone

7hViews 32Likes 1
Fuggy@CEOofFuggy

@yacineMTB I could do this without any AI

6hViews 28Likes 1
GoldenGreen@GoldenGreen_

@TACIXAT I also like how people talk exclusively through terms that have been effectively obliterated by marketing firms (compute) instead of just referring to flops - which compute used to mean, but now effectively doesn't, because it isn't a commodity in this context

6hViews 777Likes 2
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