Abstract: During learning trials, systems are exposed to different failure conditions
which may break robotic parts before a safe behavior is discovered. Humans
contour this problem by grounding their learning to a safer structure/control
first and gradually increasing its difficulty. This paper presents the impact
of a similar supports in the learning of a stable gait on a quadruped robot.
Based on the psychological theory of instructional scaffolding, we provide
different support settings to our robot, evaluated with strain gauges, and use
Bayesian Optimization to conduct a parametric search towards a stable Raibert
controller. We perform several experiments to measure the relation between
constant supports and gradually reduced supports during gait learning, and our
results show that a gradually reduced support is capable of creating a more
stable gait than a support at a fixed height. Although gaps between simulation
and reality can lead robots to catastrophic failures, our proposed method
combines speed and safety when learning a new behavior.