Learning hybrid locomotion skills-Learn to exploit residual actions and modulate model-based gait control.

ABSTRACT: This work has developed a hybrid framework that combines machine learning and control approaches for legged robots to achieve new capabilities of balancing against external perturbations. The framework embeds a kernel which is a model-based, full parametric closed-loop and analytical controller as the gait pattern generator. On top of that, a neural network with symmetric partial data augmentation learns to automatically adjust the parameters for the gait kernel, and also generate compensatory actions for all joints, thus significantly augmenting the stability under unexpected perturbations. Seven Neural Network policies with different configurations were optimized to validate the effectiveness and the combined use of the modulation of the kernel parameters and the compensation for the arm

SUBMITTER: Kasaei M 

PROVIDER: S-EPMC10123269 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature