Table of Contents Author Guidelines Submit a Manuscript
Complexity
Volume 2017, Article ID 7948684, 16 pages
https://doi.org/10.1155/2017/7948684
Research Article

Skill Learning for Intelligent Robot by Perception-Action Integration: A View from Hierarchical Temporal Memory

1School of Electrical and Information Engineering, Jinan University, Zhuhai, China
2National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

Correspondence should be addressed to Jianfen Zhang; moc.liamg@gnahzfjee

Received 3 July 2017; Accepted 7 September 2017; Published 15 November 2017

Academic Editor: Yanan Li

Copyright © 2017 Xinzheng Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Skill learning autonomously through interactions with the environment is a crucial ability for intelligent robot. A perception-action integration or sensorimotor cycle, as an important issue in imitation learning, is a natural mechanism without the complex program process. Recently, neurocomputing model and developmental intelligence method are considered as a new trend for implementing the robot skill learning. In this paper, based on research of the human brain neocortex model, we present a skill learning method by perception-action integration strategy from the perspective of hierarchical temporal memory (HTM) theory. The sequential sensor data representing a certain skill from a RGB-D camera are received and then encoded as a sequence of Sparse Distributed Representation (SDR) vectors. The sequential SDR vectors are treated as the inputs of the perception-action HTM. The HTM learns sequences of SDRs and makes predictions of what the next input SDR will be. It stores the transitions of the current perceived sensor data and next predicted actions. We evaluated the performance of this proposed framework for learning the shaking hands skill on a humanoid NAO robot. The experimental results manifest that the skill learning method designed in this paper is promising.