Passive dynamic walker controller design employing an RLS-based natural actor-critic learning algorithm

Baeksuk Chu, Daehie Hong, Jooyoung Park, Jae Hun Chung

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

A passive dynamic walker belongs to a class of bipedal walking robots that are able to walk stably down a small decline without using any actuators. The purpose of this research is to design a controller in order to build actuated robots capable of walking on a flat terrain based on passive dynamic walking. To achieve this objective, a control algorithm was used based on reinforcement learning (RL). The RL method is a goal-directed learning of a mapping from situations to actions without relying on exemplary supervision or complete models of the environment. The goal of the RL method is to maximize a reward, which is an evaluative feedback from the environment. In the process of constructing the reward of the actuated passive dynamic walker, the control objective, which is stable walking on level ground, is directly included. In this study, an RL algorithm based on the actor-critic architecture and the natural gradient method is applied. Also, the recursive least-squares (RLS) method was employed for the learning process in order to improve the efficiency of the method. The control algorithm was verified with computer simulations based on the eigenvalue analysis for stable locomotion.

Original languageEnglish
Pages (from-to)1027-1034
Number of pages8
JournalEngineering Applications of Artificial Intelligence
Volume21
Issue number7
DOIs
Publication statusPublished - 2008 Oct

Keywords

  • Actor-critic architecture
  • Natural gradient
  • Passive dynamic walker
  • Recursive least-squares (RLS)
  • Reinforcement learning (RL)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Passive dynamic walker controller design employing an RLS-based natural actor-critic learning algorithm'. Together they form a unique fingerprint.

  • Cite this