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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

MANTISBOT: A ROBOTIC PLATFORM FOR DEVELOPMENT OF COMPLEX NEURAL CONTROL

Chrzanowski, David M. 06 February 2015 (has links)
No description available.
2

MASSIVELY DISTRIBUTED NEUROMORPHIC CONTROL FOR LEGGED ROBOTS MODELED AFTER INSECT STEPPING

Szczecinski, Nicholas S. 12 March 2013 (has links)
No description available.
3

Using Learned Affordances For Robotic Behavior Development

Dogar, Mehmet Remzi 01 September 2007 (has links) (PDF)
&ldquo / Developmental robotics&rdquo / 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&#039 / s concept of &ldquo / affordances&rdquo / 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.

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