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This module is a python implementation of IT&E.

IT&E code for developing hexapod experiments similar to Cully et al. (2015), Nature. This code is not replicating the exact experiments of the Nature paper, but can be seen as a more modern implementation of the underlying algorithm.

Citing this code

If you use our code for a scientific paper, please cite:

Antoine Cully, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret. "Robots that can adapt like animals." Nature 521, no. 7553 (2015): 503-507.

In BibTex:

    title = {Robots that can adapt like animals},
    volume = {521},
    pages = {503--507},
    number = {7553},
    journal = {Nature},
    author = {Cully, Antoine and Clune, Jeff and Tarapore, Danesh and Mouret, Jean-Baptiste},
    year = {2015}

Here are some IT&E use cases. You can click on the GIFs to watch the full videos.

How to install ?

Dependencies (use pip or your favorite package manager)

  • python3
  • matplotlib
  • pybullet
  • numpy
  • tensorflow
  • gym

Clone this repository and run :

git submodule update --init --recursive

Use with simulation

IT&E will be used with the maps contained in maps folter. The damages used here are defined in the pybullet_minitaur_sim and pyhexapod repositories.

To create new maps or change the damages please refer to the pybullet_minitaur_sim and pyhexapod repository.

You can disable the gui by modifying the params at the end of

For the minitaur

python maps/minitaur/centroids_40000_16.dat maps/minitaur/archive_20000.dat 0 minitaur

For the hexapod :

python maps/hexapod/centroids_40000_6.dat maps/hexapod/archive_20000.dat 0 hexapod

Use with the real robot

For the minitaur

In order to run it with the real minitaur robot please refer to the minitaur_framework

Note that has been configured to test 10 different maps from 10 different experiments

For the hexapod :

In order to run it with the real pexod robot, you will need to use the C++ API : IT&E


python implementation of IT&E






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