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