SenseNet is a dataset of touchable 3D objects and a sensorimotor simulator to teach AIs how to interact with their environments via sensorimotor systems and touch neurons. SenseNet is meant as a research tool for computational neuroscientists and machine learning researchers.

You can easily run your own experiments with our environments and agents or you can build your own

for more information, visit http://sensenet.ai

We currently support Mac OS X and Linux (ubuntu 14.04). Windows should work, but we have not tested it. We also have vagrant/virtualbox images for you to run an any platform that supports virtualbox

you will need python3 (python2 might work, but has not been tested), numpy, and pybullet

pip install sensenet (still working on this)

git clone http://github.com/jtoy/sensenet you can run "pip install -r requirements.txt" to install all the python software dependencies pip install -e '.[all]'

You can run all the code in a vagrant/virtualbox. To use the image, install vagrant and virtualbox. Then run:

vagrant up once inside the image, you can run "cd /vagrant && python3 test.py"

You can use the SenseNet dataset or your own dataset. Download the dataset from http://sensenet.ai by default the dataset is expected to be in this structure:

sensenet_main_dir sensenet_main_dir/sensenet/ #git cloned here sensenet_main_dir/sensenet_data/ #dataset here sensenet_main_dir/sensenet_data/objects sensenet_main_dir/sensenet_data/objects/0 sensenet_main_dir/sensenet_data/objects/1 sensenet_main_dir/sensenet_data/objects/2

#the numbers in objects represent the class number for the objects

##Testing we use pytest to run tests, ro tun the tests just type "pytest" from the root directory

Included with SenseNet are several examples for competing on the benchmark "blind object classification" There is a pytorch example and a tensorflow example. to run them: cd agents && python reinforce.py

to see the graphs: tensorboard --logdir runs then go to your browser at http://localhost:6000/