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Gait-phase Estimation Module

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# gem
Gait-phase Estimation Module (GEM) for Humanoid Robot Walking. The code is open-source (BSD License). Please note that this work is an on-going research and thus some parts are not fully developed yet. Furthermore, the code will be subject to changes in the future which could include greater re-factoring.

GEM is an unsupervised learning framework which employs a 2D latent space obtained with PCA and Gaussian Mixture Models (GMMs) to facilitate accurate prediction/classification of the gait phase during locomotion.

Video: https://www.youtube.com/watch?v=w09yb81IXpQ

Papers: 
* Unsupervised Gait Phase Estimation for Humanoid Robot Walking (Intl. Conf. on Robotics and Automation (ICRA), 2019)

GEM functionalities have been encapsulated in the GEM2 package (https://github.com/mrsp/gem2). This package is now deprecated.
 
## Training
Solely proprioceptive sensing is utilized in training, namely joint encoder, F/T, and IMU.

## Real-time Gait-Phase Prediction
GEM can be readily employed in real-time for estimating the gait phase. The latter is accomplished by either loading a trained GEM python module and use it for real-time preditiction or by utilizying GEM for real-time estimation based on the sensed contact wrenches and optionally leg kinematics.

## Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

## Prerequisites
* Ubuntu 16.04 and later
* ROS kinetic and later
* Sklearn 
* Keras 2.2.4
* tensorflow 
* tested on python3 (3.6.9) and python (2.7.17)

## Installing
* pip install tensorflow
* pip install keras
* pip install sklearn
* git clone https://github.com/mrsp/gem.git
* catkin_make
* If you are using catkin tools run: catkin build

## ROS Examples
### Train the Valkyrie module
* train: python train.py ../config/gem_params.yaml

### Train your own module
* Save the corresponding files in a similar form as the valkyrie files 
* train: python train.py ../config/gem_params_your_robot.yaml

### Run in real-time to infer the gait-phase:
* configure appropriately the config yaml file (in config folder) with the corresponding topics 
* roslaunch gem gem_ros.launch

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