  In this paper, we present a model-free learning-based control scheme for the
soft snake robot to improve its contact-aware locomotion performance in a
cluttered environment. The control scheme includes two cooperative controllers:
A bio-inspired controller (C1) that controls both the steering and velocity of
the soft snake robot, and an event-triggered regulator (R2) that controls the
steering of the snake in anticipation of obstacle contacts and during contact.
The inputs from the two controllers are composed as the input to a Matsuoka CPG
network to generate smooth and rhythmic actuation inputs to the soft snake. To
enable stable and efficient learning with two controllers, we develop a
game-theoretic process, fictitious play, to train C1 and R2 with a shared
potential-field-based reward function for goal tracking tasks. The proposed
approach is tested and evaluated in the simulator and shows significant
improvement of locomotion performance in the obstacle-based environment
comparing to two baseline controllers.
