Excited to share our work on Morphogenesis in Minecraft! We show that neural cellular automata can learn to grow not only complex 3D artifacts with over 3,000 blocks but also functional Minecraft machines that can regenerate when cut in half 🐛🔪=🐛🐛

PDF:arxiv.org/abs/2103.08737
Very proud of the team @SudhakaranShyam , @DjordjeGrbic2, @Sylvia_Sparkle, @AdamKat0na, @enasmel, @claire__aoi. Hopefully of interest to @kenneth0stanley, @Smearle_RH, @pyoudeyer, @BertChakovsky. Maybe Minecraft+NCAs will be the building block of @gregeganSF's permutation city?😄 Image
Inspired by @zzznah, @RandazzoEttore, @eyvindn, @drmichaellevin, @blaiseaguera, @ch402 work, we train a network in a supervised way to grow target structures, extending their approach from 2D to 3D.
To our surprise, the approach was able to generate diverse and complex interiors and in the case of JungleTemple, the NCA even generates a functional arrow trap, which uses a working redstone circuit! Image
You might have wondered what happened to the poor caterpillar we cut in half. It regenerated thanks to the power of NCA! Image
Does it always work? No. When things become very complex and less regular, like the cathedral with 3584 blocks, there can be some interesting artefacts.
Our paper broadens the scope of tasks suitable for NCAs. Although the work is in a simplified 3D environment, we hope the model’s capability of generating increasingly complex 3D entities brings us one step closer to real-life, self-organizing, and regenerative physical artefacts
This work uses our EvoCraft API. We will release the code for our NCA soon as well.

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with Sebastian Risi

Sebastian Risi Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @risi1979

7 Jul 20
@enasmel and myself are excited to announce our paper "Meta-Learning through Hebbian Plasticity in Random Networks" arxiv.org/abs/2007.02686

Instead of optimizing the neural network's weights directly, we only search for synapse-specific Hebbian learning rules. Thread 👇
Starting from completely random weights, the discovered Hebbian rules enable an agent to navigate a dynamical 2D-pixel environment; likewise they allow a simulated 3D quadrupedal robot to learn how to walk in around 40 timesteps in the absence of any explicit reward.
The random Hebbian network is also able to adapt to damages in the morphology of the quadrupedal robot, while a fixed-weight network fails to do so. Image
Read 5 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!