To read the full version of this content please select one of the options below:

Robotic autonomous behavior selection using episodic memory and attention system

Dong Liu (School of Mechanical Engineering, Dalian University of Technology, Dalian, China)
Ming Cong (School of Mechanical Engineering, Dalian University of Technology, Dalian, China)
Yu Du (Department of Mechanical Engineering, University of British Columbia, Vancouver, Canada)
Qiang Zou (School of Mechanical Engineering, Dalian University of Technology, Dalian, China)
Yingxue Cui (School of Mechanical Engineering, Dalian University of Technology, Dalian, China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 15 May 2017

Abstract

Purpose

This paper aims to focus on the autonomous behavior selection issue of robotics from the perspective of episodic memory in cognitive neuroscience with biology-inspired attention system. It instructs a robot to follow a sequence of behaviors. This is similar to human travel to a target location by guidance.

Design/methodology/approach

The episodic memory-driving Markov decision process is proposed to simulate the organization of episodic memory by introducing neuron stimulation mechanism. Based on the learned episodic memory, the robotic global planning method is proposed for efficient behaviors sequence prediction using bottom-up attention. Local behavior planning based on risk function and feasible paths is used for behavior reasoning under imperfect memory. Aiming at the problem of whole target selection under redundant environmental information, a top-down attention servo control method is proposed to effectively detect the target containing multi-parts and distractors which share same features with the target.

Findings

Based on the proposed method, the robot is able to accumulate experience through memory, and achieve adaptive behavior planning, prediction and reasoning between tasks, environment and threats. Experimental results show that the method can balance the task objectives, select the suitable behavior according to current environment.

Originality/value

The behavior selection method is integrated with cognitive levels to generate optimal behavioral sequence. The challenges in robotic planning under uncertainty and the issue of target selection under redundant environment are addressed.

Keywords

Acknowledgements

The project received support from the National Natural Science Foundation of China (61503057) and the Fundamental Research Funds for the Central Universities (DUT16RC(4)31).

Citation

Liu, D., Cong, M., Du, Y., Zou, Q. and Cui, Y. (2017), "Robotic autonomous behavior selection using episodic memory and attention system", Industrial Robot, Vol. 44 No. 3, pp. 353-362. https://doi.org/10.1108/IR-09-2016-0250

Publisher

:

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited