Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Using learned affordances for robotic behavior development
Download
index.pdf
Date
2007
Author
Doğar, Mehmet Remzi
Metadata
Show full item record
Item Usage Stats
197
views
134
downloads
Cite This
“Developmental robotics” proposes that, instead of trying to build a robot that shows intelligence once and for all, what one must do is to build robots that can develop. A robot should go through cognitive development just like an animal baby does. These robots should be equipped with behaviors that are simple but enough to bootstrap the system. Then, as the robot interacts with its environment, it should display increasingly complex behaviors. Studies in developmental psychology and neurophysiology provide support for the view that, the animals start with innate simple behaviors, and develop more complex behaviors through the differentiation, sequencing, and combination of these primitive behaviors. In this thesis, we propose such a development scheme for a mobile robot. J.J. Gibson's concept of “affordances” provides the basis of this development scheme, and we use a formalization of affordances to make the robot learn about the dynamics of its interactions with its environment. We show that an autonomous robot can start with pre-coded primitive behaviors, and as it executes its behaviors randomly in an environment, it can learn the affordance relations between the environment and its behaviors. We then present two ways of using these learned structures, in achieving more complex, voluntary behaviors. In the first case, the robot still uses its pre-coded primitive behaviors only, but the sequencing of these are such that new more complex behaviors emerge. In the second case, the robot uses its pre-coded primitive behaviors to create new behaviors.
Subject Keywords
Computer Engineering.
,
Computer Hardware
URI
http://etd.lib.metu.edu.tr/upload/12608695/index.pdf
https://hdl.handle.net/11511/17066
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Using learned affordances for robotic behavior development
Doǧar, Mehmet R.; Ugur, Emre; Şahin, Erol; Çakmak, Maya (2008-09-18)
“Developmental robotics” proposes that, instead of trying to build a robot that shows intelligence once and for all, what one must do is to build robots that can develop. These robots should be equipped with behaviors that are simple but enough to bootstrap the system. Then, as the robot interacts with its environment, it should display increasingly complex behaviors. In this paper, we propose such a development scheme for a mobile robot. J.J. Gibson’s concept of “affordances” provides the basis of this dev...
Evolving aggregation behaviors for swarm robotics systems: a systematic case study
Bahçeci, Erkin; Şahin, Erol; Department of Computer Engineering (2005)
Evolutionary methods are shown to be useful in developing behaviors in robotics. Interest in the use of evolution in swarm robotics is also on the rise. However, when one attempts to use artificial evolution to develop behaviors for a swarm robotic system, he is faced with decisions to be made regarding some parameters of fitness evaluations and of the genetic algorithm. In this thesis, aggregation behavior is chosen as a case, where performance and scalability of aggregation behaviors of perceptron control...
Vision-based robot localization using artificial and natural landmarks
Arıcan, Zafer; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2004)
In mobile robot applications, it is an important issue for a robot to know where it is. Accurate localization becomes crucial for navigation and map building applications because both route to follow and positions of the objects to be inserted into the map highly depend on the position of the robot in the environment. For localization, the robot uses the measurements that it takes by various devices such as laser rangefinders, sonars, odometry devices and vision. Generally these devices give the distances o...
A fluid dynamics framework for control of mobile robot networks
Paç, Muhammed Raşid; Erkmen, Aydan Müşerref; Department of Electrical and Electronics Engineering (2007)
This thesis proposes a framework for controlling mobile robot networks based on a fluid dynamics paradigm. The approach is inspired by natural behaviors of fluids demonstrating desirable characteristics for collective robots. The underlying mathematical formalism is developed through establishing analogies between fluid bodies and multi-robot systems such that robots are modeled as fluid elements that constitute a fluid body. The governing equations of fluid dynamics are adapted to multi-robot systems and a...
Learning to Navigate Endoscopic Capsule Robots
Turan, Mehmet; Almalioglu, Yasin; Gilbert, Hunter B.; Mahmood, Faisal; Durr, Nicholas J.; Araujo, Helder; Sari, Alp Eren; Ajay, Anurag; Sitti, Metin (Institute of Electrical and Electronics Engineers (IEEE), 2019-07-01)
Deep reinforcement learning (DRL) techniques have been successful in several domains, such as physical simulations, computer games, and simulated robotic tasks, yet the transfer of these successful learning concepts from simulations into the real world scenarios remains still a challenge. In this letter, a DRL approach is proposed to learn the continuous control of a magnetically actuated soft capsule endoscope (MASCE). Proposed controller approach can alleviate the need for tedious modeling of complex and ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. R. Doğar, “Using learned affordances for robotic behavior development,” M.S. - Master of Science, Middle East Technical University, 2007.