A novel Q-Learning Based Cartesian Model Reference Compliance Controller Implementation for a Humanoid Robotic Arm

SG Khan, G Herrmann, F.L Lewis, CR Melhuish, AG Pipe

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

5 Citations (Scopus)

Abstract

This paper presents the implementation (real time and simulation) of a model-free Q-learning based discrete model reference compliance controller for a humanoid robot arm. The Reinforcement learning (RL) scheme uses a recently developed Q-learning scheme to develop an optimal policy on-line. The RL Cartesian (x and y) tracking controller with model reference compliance was implemented using two links (shoulder flexion and elbow flexion joints) of the right arm of the humanoid Bristol-Elumotion-Robotic-Torso II (BERT II) torso.
Translated title of the contributionA novel Q-Learning Based Cartesian Model Reference Compliance Controller Implementation for a Humanoid Robotic Arm
Original languageEnglish
Title of host publication5th IEEE International Conference on Robotics, Automation and Mechatronics (RAM), Qingdao
DOIs
Publication statusPublished - 2011

Bibliographical note

Conference Organiser: IEEE

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