Adapting stiffness and attack angle through trial and error to increase self-stability in locomotion.

Biological systems are outperforming machines in legged locomoting under almost any conditions. This is partly due to their capability of learning from failure and adapting their control approach and morphological features. This paper proposes an approach that extends the spring-loaded inverted pendulum (SLIP) model with the capability to adapt its attack angle (control) and stiffness (morphology) based on previous locomotion attempts. A set of different update rules, i.e., how this experience is used to adapt, are systematically investigated. The results suggest that modifying either attack angle, or stiffness, or both is beneficial with respect to achieve stable locomotion. Particularly, if the current system configuration (control and morphology) outperforms the previous one, the results suggest that increasing the angle and decreasing the stiffness of the system leads to more stable solutions. Consequently, the basic SLIP model extended by the proposed learning capabilities is able to reach stable locomotion over a much wider range of parameter combinations simply through trial and error.