Gait-phase Estimation Module
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:
GEM functionalities have been encapsulated in the GEM2 package (https://github.com/mrsp/gem2). This package is now deprecated.
Solely proprioceptive sensing is utilized in training, namely joint encoder, F/T, and IMU.
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.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.