Hierarchical behavior categorization using correlation based adaptive resonance theory

Download

index.pdf

Date

2011

Author

Yavaş, Mustafa

Metadata

Show full item record

Item Usage Stats

260
views

294
downloads

This thesis introduces a novel behavior categorization model that can be used for behavior recognition and learning. Correlation Based Adaptive Resonance Theory (CobART) network, which is a kind of self organizing and unsupervised competitive neural network, is developed for this purpose. CobART uses correlation analysis methods for category matching. It has modular and simple architecture. It can be adapted to different categorization tasks by changing the correlation analysis methods used when needed. CobART networks are integrated hierarchically for an adequate categorization of behaviors. The hierarchical model is developed by adding a second layer CobART network on top of first layer networks. The first layer CobART networks categorize self behavior data of a robot or an object in the environment. The second layer CobART network receives first layer CobART network categories as an input, and categorizes them to elicit the robot's behavior with respect to its effect on the object. Besides, the second layer network back-propagates the matching information to the first layer networks in order to find the relation between the first layer categories. The performance of the hierarchical model is compared with that of different neural network based models. Experiments show that the proposed model generates reasonable categorization of behaviors being tested. Moreover, it can learn different forms of the behaviors, and it can detect the relations between them. In essence, the model has an expandable architecture and it contains reusable parts. The first layer CobART networks can be integrated with other CobART networks for another categorization task. Hence, the model presents a way to reveal all behaviors performed by the robot at the same time.

Subject Keywords

Robots, Machine theory.

URI

http://etd.lib.metu.edu.tr/upload/12613828/index.pdf
https://hdl.handle.net/11511/21122

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Simple and complex behavior learning using behavior hidden Markov Model and CobART Seyhan, Seyit Sabri; Alpaslan, Ferda Nur; Department of Computer Engineering (2013) In this thesis, behavior learning and generation models are proposed for simple and complex behaviors of robots using unsupervised learning methods. Simple behaviors are modeled by simple-behavior learning model (SBLM) and complex behaviors are modeled by complex-behavior learning model (CBLM) which uses previously learned simple or complex behaviors. Both models have common phases named behavior categorization, behavior modeling, and behavior generation. Sensory data are categorized using correlation based...
Momentum transfer continuum between preshape and grasping based on fluidics Özyer, Barış; Erkmen, İsmet; Erkmen, Aydan Müşerref; Department of Electrical and Electronics Engineering (2012) This dissertation propose a new fluidics based framework to determine a continuum between preshaping and grasping so as to appropriately preshape a multi-fingered robot hand for creating an optimal initialization of grasp. The continuum of a hand preshape closing upon an object that creates an initial object motion tendency of the object based on the impact moment patterns generated from the fingers is presented. These motion tendencies should then be suitable for the proper initiation of the grasping task....
Hierarchical behavior categorization using correlation based adaptive resonance theory Yavaş, Mustafa; Alpaslan, Ferda Nur (2012-02-01) This paper introduces a new model for robot behavior categorization. Correlation based adaptive resonance theory (CobART) networks are integrated hierarchically in order to develop an adequate categorization, and to elicit various behaviors performed by the robot. The proposed model is developed by adding a second layer CobART network which receives first layer CobART network categories as an input, and back-propagates the matching information to the first layer networks. The first layer CobART networks cat...
Signal injection with perceptual criteria Tuncer, Temel Engin (1998-12-01) In this paper, a novel method for increasing the coding performance and information transmission capacity is presented. This method is mainly based on perceptual modelling of input signal such as speech or audio. Presented approach may be seen as an alternative to transforms which dynamically change analysis window for better energy compaction. A perceptual model is established in order to obtain a global masking threshold in frequency below which sounds become inaudible. Certain criteria are developed for ...
Imitation of human body poses and hand gestures using a particle based fluidics method Tilki, Umut; Erkmen, İsmet; Erkmen, Aydan Müşerref; Department of Electrical and Electronics Engineering (2012) In this thesis, a new approach is developed, avoiding the correspondence problem caused by the difference in embodiment between imitator and demonstrator in imitation learning. In our work, the imitator is a fluidic system of dynamics totally different than the imitatee, which is a human performing hand gestures and human body postures. The fluidic system is composed of fluid particles, which are used for the discretization of the problem domain. In this work, we demonstrate the fluidics formation control s...

Citation Formats

M. Yavaş, “Hierarchical behavior categorization using correlation based adaptive resonance theory,” Ph.D. - Doctoral Program, Middle East Technical University, 2011.