  Humans are able to outperform robots in terms of robustness, versatility, and
learning of new tasks in a wide variety of movements. We hypothesize that
highly nonlinear muscle dynamics play a large role in providing inherent
stability, which is favorable to learning. While recent advances have been made
in applying modern learning techniques to muscle-actuated systems both in
simulation as well as in robotics, so far, no detailed analysis has been
performed to show the benefits of muscles when learning from scratch. Our study
closes this gap and showcases the potential of muscle actuators for core
robotics challenges in terms of data-efficiency, hyperparameter sensitivity,
and robustness.

Robotics;Machine Learning