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

Xinzheng Zhang, Jianfen Zhang (Corresponding Author), Junpei Zhong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Article number7948684
JournalComplexity
Volume2017
DOIs
Publication statusPublished - 15 Nov 2017
Externally publishedYes

ASJC Scopus subject areas

  • Computer Science(all)
  • General

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