  This work aims to combine machine learning and control approaches for legged
robots, and developed a hybrid framework to achieve new capabilities of
balancing against external perturbations. The framework embeds a kernel which
is a fully parametric closed-loop gait generator based on analytical control.
On top of that, a neural network with symmetric partial data augmentation
learns to automatically adjust the parameters for the gait kernel and to
generate compensatory actions for all joints as the residual dynamics, thus
significantly augmenting the stability under unexpected perturbations. The
performance of the proposed framework was evaluated across a set of challenging
simulated scenarios. The results showed considerable improvements compared to
the baseline in recovering from large external forces. Moreover, the produced
behaviours are more natural, human-like and robust against noisy sensing.
