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

TL;DR

This paper introduces a scaffolded learning approach for quadruped robot gait development, using Bayesian Optimization and support reduction to enhance stability and safety during learning.

Contribution

It applies psychological scaffolding principles to robotic gait learning, demonstrating that gradually reducing support improves stability over fixed support methods.

Findings

Gradually reduced support yields more stable gait.

Bayesian Optimization effectively tunes gait parameters.

Supports enhance safety during learning process.

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.