Scenario and context specific visual robot behavior learning

2011 ◽  
Author(s):  
Krishna Kumar Narayanan ◽  
Luis Felipe Posada ◽  
Frank Hoffmann ◽  
Torsten Bertram
Keyword(s):  
Robot Behavior ◽  
Behavior Learning ◽  
Context Specific

Experimental Study on Behavior Acquisition of Mobile Robot by Deep Q-Network

2017 ◽  
Vol 21 (5) ◽  
pp. 840-848 ◽  
Author(s):  
Hikaru Sasaki ◽  
Tadashi Horiuchi ◽  
Satoru Kato ◽  
◽  
◽  
...  
Keyword(s):  
Mobile Robot ◽  
Visual Information ◽  
Profit Sharing ◽  
Simulation Software ◽  
Learning Performance ◽  
Simulation Environment ◽  
Q Learning ◽  
Robot Behavior ◽  
Target Network ◽  
Behavior Learning

Deep Q-network (DQN) is one of the most famous methods of deep reinforcement learning. DQN approximates the action-value function using Convolutional Neural Network (CNN) and updates it using Q-learning. In this study, we applied DQN to robot behavior learning in a simulation environment. We constructed the simulation environment for a two-wheeled mobile robot using the robot simulation software, Webots. The mobile robot acquired good behavior such as avoiding walls and moving along a center line by learning from high-dimensional visual information supplied as input data. We propose a method that reuses the best target network so far when the learning performance suddenly falls. Moreover, we incorporate Profit Sharing method into DQN in order to accelerate learning. Through the simulation experiment, we confirmed that our method is effective.

KSU-IMR Mobile Robot Navigation Maps Building and Learning

2015 ◽  
Vol 789-790 ◽  
pp. 717-722
Author(s):  
Ebrahim Mattar ◽  
K. Al Mutib ◽  
M. AlSulaiman ◽  
Hedjar Ramdane
Keyword(s):  
Mobile Robot ◽  
Dimensionality Reduction ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Research Outcomes ◽  
Neuro Fuzzy ◽  
Robot Behavior ◽  
Behavior Learning ◽  
Mobile Maps

It is essential to learn a robot navigation environment. We describe research outcomes for KSU-IMR mapping and intelligence. This is for navigating and robot behavior learning. The mobile maps learning and intelligence was based on hybrid paradigms and AI functionaries. Intelligence was based on ANN-PCA for dimensionality reduction, and Neuro-Fuzzy architecture.

Feature Extraction of Robot Sensor Data Using Factor Analysis for Behavior Learning

2004 ◽  
Vol 8 (3) ◽  
pp. 284-294
Author(s):  
Wai-keung Fung ◽  
◽  
Yun-hui Liu
Keyword(s):  
Factor Analysis ◽  
Feature Extraction ◽  
Sensor Data ◽  
Range Data ◽  
Perceptual Space ◽  
Latent Factors ◽  
New Approach ◽  
Robot Behavior ◽  
Behavior Learning ◽  
Sensor Information

The paper addresses feature extraction of sensor data for robot behavior learning using factor analysis. Redundancies in sensor types and quantities are common in sensing competence of robots. The redundancies cause the high dimensionality of the perceptual space. It is impractical to incorporate all available sensor information in decision-making and learning of robots due to the huge memory and computational requirements. This paper proposes a new approach to extract important knowledge from sensor data based on the inter-correlation of sensor data using factor analysis and construct logical perceptual space for robot behavior learning. The logical perceptual space is constructed by hypothetical latent factors extracted using factor analysis. Since the latent factors extracted have fewer dimensions than raw sensor data, using the logical perceptual space in behavior learning would significantly simplify the learning process and architecture. Experiments have been conducted to demonstrate the process of logical perceptual space extraction from ultrasonic range data for robot behavior learning.

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