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During learning trials, systems are exposed to different failure conditionswhich may break robotic parts before a safe behavior is discovered. Humanscontour this problem by grounding their learning to a safer structure/controlfirst and gradually increasing its difficulty. This paper presents the impactof a similar supports in the learning of a stable gait on a quadruped robot.Based on the psychological theory of instructional scaffolding, we providedifferent support settings to our robot, evaluated with strain gauges, and useBayesian Optimization to conduct a parametric search towards a stable Raibertcontroller. We perform several experiments to measure the relation betweenconstant supports and gradually reduced supports during gait learning, and ourresults show that a gradually reduced support is capable of creating a morestable gait than a support at a fixed height. Although gaps between simulationand reality can lead robots to catastrophic failures, our proposed methodcombines speed and safety when learning a new behavior.

Scaffolded Learning of In-place Trotting Gait for a Quadruped Robot with Bayesian Optimization