This is a library of basic functions for sensorimotor learning experiments using simulators and real robots for my thesis project Robotic self-exploration and acquisition of sensorimotor primitives. The basic idea there is to have states that combine sensors, actions, motor signals and inferred intermediate states into a unified space and learn predictive models from incoming data in that space to infer predictive actions from the approximated sensorimotor dynamics.
This repository is still in an early stage of release. Originally pushed on the occasion of sharing the smp_sphero code [1] which depends on this. Incremental clean-up and refactoring as well as consolidating with other existing repositories in smp land. Available functions, naming and API all subject to change.
The first bit are the standard libraries such as numpy, scipy, matplotlib, and sklearn which are needed regardless. You can either install them via package manager
sudo apt install python3-numpy python3-scipy python3-matplotlib python3-sklearn
Ultimately optional but recommended modules are rlspy for a recursive least squares implementation and jpype, a java to python bridge.
rlspy is used as a reference update rule for prediction learning in many models. Get it from https://github.com/bluesquall/rlspy by cloning into your filesystem and
cd rlspy
sudo pip3 install .
Then set the RLSPY variable in smp_base/config.py.
Get jpype with sudo apt install python3-jpype or sudo pip3 install jpype1.
This is needed for computing information theoretic measures with the Java Information Dynamics Toolkit available from https://github.com/jlizier/jidt. Download the latest distribution zip from there, unpack it and set the JARLOC variable in smp_base/config.py to point to the absolte path of infodynamics.jar
Another measure that is used is the earth mover's distance, currently there's two different implementations used: emd and pyemd.
emd from https://github.com/garydoranjr/pyemd, that is git clone it,
cd pyemd
sudo pip3 install .
should install package emd.
pyemd comes via pip by running
sudo pip3 install pyemd
Additional packages we depend on at various places are pandas, ROS, pyunicorn, mdp, but they can be installed later. Pandas and MDP can be had from the distro with
apt-get install python-pandas python-mdp
Oger is an extension for MDP and can be obtained from http://reservoir-computing.org/installing_oger.
For installing a basic ROS stack see the wiki at https://www.ros.org/wiki, you need to have the package python-rospy (Recently I have been building a minimal py3 version of ROS from source).
Pyunicorn is used for recurrence analysis and can be obtained from source at https://github.com/pik-copan/pyunicorn or via pip.
otl: online temporal learning library by harold soh w/ gaussian process reservoir readout, loca fork on https://github.com/x75/otl
pypr: gaussian mixture models with conditional inference, local fork on https://github.com/x75/pypr
igmm: incremental gaussian mixture models, https://github.com/yumilceh/igmm
IncSfa: incremental slow feature analysis, untested https://github.com/varunrajk/IncSFA, https://github.com/Kaixhin/IncSFA
In a freshly cloned repository a local configuration file has to be created from a template. To do this
cp smp_base/config.py.dist smp_base/config.py
and then edit the file smp_base/config.py and set the JARLOC and RLSPY variables to matching values. Use the absolute path so they can be found from anywhere.
Non stringent unit testing implemented yet. Most libary files have a main method that runs some test. Work-in-progress.
.. todoList::