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

arXiv: https://arxiv.org/abs/2101.09961 · HTML: https://mlsanity.com/paper/2101.09961/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 1 of 1)

- [Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped](https://mlsanity.com/paper/1709.06047/) (2017)

## Abstract (arXiv)

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.