Towards an on-line neural conditioning model for mobile robots

Date

2001-01-01

Author

Şahin, Erol

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

209
views

0
downloads

This paper presents a neural conditioning model for on-line learning of behaviors on mobile robots. The model is based on Grossberg's neural model of conditioning as recently implemented by Chang and Gaudiano. It attempts to tackle some of the limitations of the original model by (1) using a temporal difference of the reinforcement to drive learning, (2) adding eligibility trace mechanisms to dissociate behavior generation from learning, (3) automatically categorizing sensor readings and (4) bootstrapping the learning process through the use of unconditioned responses. Preliminary results of the model that learn simple behaviors on a mobile robot simulator are presented.

URI

https://hdl.handle.net/11511/47915

DOI

https://doi.org/10.1007/3-540-45723-2_63

Collections

Department of Computer Engineering, Conference / Seminar

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...
Towards Group Cognitive Analysis of Collaborative Learning with Eye Tracking and Brain Imaging Technologies Çakır, Murat Perit (null; 2013-06-15) This paper aims to motivate the need for a group cognitive approach for the multimodal analysis of learning to better account for neural and ocular correlates of collaborative learning. In reference to related work in the emerging fields of two-person neuroscience and dual-eye tracking, the paper argues for the need for moving from a singlemind perspective to a group cognitive perspective where neural and ocular correlates of joint meaning making are investigated in the context of sequentially unfolding soc...
Simple and complex behavior learning using behavior hidden Markov model and CobART Seyhan, Seyit Sabri; Alpaslan, Ferda Nur; Yavaş, Mustafa (2013-03-01) This paper proposes behavior learning and generation models for simple and complex behaviors of robots using unsupervised learning methods. While the simple behaviors are modeled by simple-behavior learning model (SBLM), complex behaviors are modeled by complex-behavior learning model (CBLM) which uses previously learned simple or complex behaviors. Both models include behavior categorization, behavior modeling, and behavior generation phases. In the behavior categorization phase, sensory data are categoriz...
Parametrization of Robotic Deburring Process with Motor Skills from Motion Primitives of Human Skill Model Parvizi, Payam; Ugurlu, Musab Cagri; Açıkgöz, Kemal; Konukseven, Erhan İlhan (2017-08-31) In this paper, a new method for learning robotic deburring process motor skills from human skill model is presented. The skill of a human expert is obtained by using the human skill model. This model is implemented in robot control systems that make robots imitate human skills in deburring process tasks. Based on deburring process, family of basic pattern motions are listed by identifying the movement in the task (e.g. circular, straight line and sharp corners). These motions depend on a number of complex p...
Mobile Robot Heading Adjustment Using Radial Basis Function Neural Networks Controller and Reinforcement Learning BAYAR, GÖKHAN; Konukseven, Erhan İlhan; Koku, Ahmet Buğra (2008-10-28) This paper proposes radial basis function neural networks approach to the Solution of a mobile robot heading adjustment using reinforcement learning. In order to control the heading of the mobile robot, the neural networks control system have been constructed and implemented. Neural controller has been charged to enhance the control system by adding some degrees of strength. It has been achieved that neural networks system can learn the relationship between the desired directional heading and the error posi...

Citation Formats

E. Şahin, “Towards an on-line neural conditioning model for mobile robots,” 2001, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47915.