By Topic

Human-robot collaborative manipulation through imitation and reinforcement learning

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Gu, Ye ; Dept. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA ; Thobbi, Anand ; Weihua Sheng

This paper proposes a two-phase learning framework for human-robot collaborative manipulation tasks. A table-lifting task performed jointly by a human and a humanoid robot is considered. In order to perform the task, the robot should learn to hold the table at a suitable position and then perform the lifting task cooperatively with the human. Accordingly, learning is split into two phases. The first phase enables the robot to reach out and hold one end of the table. A Programming by Demonstration (PbD) algorithm based on GMM/GMR is used to accomplish this. In the second phase the robot switches its role to an agent learning to collaborate with the human on the task. A guided reinforcement learning algorithm is developed. Using the proposed framework, the robot can successfully learn to reach and hold the table and keep the table horizontal during lifting it up with human in a reasonable amount of time.

Published in:

Information and Automation (ICIA), 2011 IEEE International Conference on

Date of Conference:

6-8 June 2011