A human-robot collaborative reinforcement learning algorithm

Uri Kartoun, Helman Stern, Yael Edan

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

This paper presents a new reinforcement learning algorithm that enables collaborative learning between a robot and a human. The algorithm which is based on the Q(λ) approach expedites the learning process by taking advantage of human intelligence and expertise. The algorithm denoted as CQ(λ) provides the robot with self awareness to adaptively switch its collaboration level from autonomous (self performing, the robot decides which actions to take, according to its learning function) to semi-autonomous (a human advisor guides the robot and the robot combines this knowledge into its learning function). This awareness is represented by a self test of its learning performance. The approach of variable autonomy is demonstrated and evaluated using a fixed-arm robot for finding the optimal shaking policy to empty the contents of a plastic bag. A comparison between the CQ(λ) and the traditional Q(λ)-reinforcement learning algorithm, resulted in faster convergence for the CQ(λ) collaborative reinforcement learning algorithm.

Original languageEnglish
Pages (from-to)217-239
Number of pages23
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Volume60
Issue number2
DOIs
StatePublished - 1 Nov 2010

Keywords

  • Human-robot collaboration
  • Reinforcement learning
  • Robot learning

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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