Publication

Evolvability of Neuromodulated Learning for Robots

Publications associées (33)

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search

Neuroevolution: from architectures to learning

Dario Floreano, Claudio Mattiussi, Peter Dürr

Artificial neural networks are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and ...

2008

Evolving Neuromodulatory Topologies for Reinforcement Learning-like Problems

Dario Floreano, Claudio Mattiussi, Peter Dürr, Andrea Soltoggio

Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions, animals capable of adaptation and learning derive an advantage. Recent studies suggest that neuromodulatory dynamics are a key factor in reg ...

IEEE Press2007

Dynamically reconfigurable bio-inspired hardware

During the last several years, reconfigurable computing devices have experienced an impressive development in their resource availability, speed, and configurability. Currently, commercial FPGAs offer the possibility of self-reconfiguring by partially modi ...

EPFL2006

Sensorless Speed Control of Traveling Wave Ultrasonic Motor

Yves Perriard, Markus Flückiger, Matteo Bullo

Ultrasonic motors are a good alternative to electromagnetic motors in medical robotics, since they are electromagnetically compatible. Estimating speed instead of using encoders reduces cost and dimension of the robot on the one hand and increases reliabil ...

2006

View Sensitive Cells as a Neural Basis for the Representation of Others in a Self-Centered Frame of Reference

Aude Billard, Eric Sauser

This work follows from a research project, in which we investigate the underlying mechanisms of human imitation and develop a neural model of its core neural circuits. The present paper presents a model of a neural mechanism by which an imitator agent can ...

2005

Hardware spiking neural network with run-time reconfigurable connectivity in an autonomous robot

Dario Floreano, Yann Thoma, Daniel Roggen

A cellular hardware implementation of a spiking neural network with run-time reconfigurable connectivity is presented. It is implemented on a compact custom FPGA board which provides a powerful reconfigurable hardware platform for hardware and software des ...

2003

Neural Morphogenesis, Synaptic Plasticity, and Evolution

Dario Floreano, Joseba Urzelai

Morphology plays an important role in the computational properties of neural systems, affecting both their functionality and the way in which this functionality is developed during life. In computer-based models of neural networks, artificial evolution is ...

2001

Conception de structures neuronales pour le contrôle de robots mobiles autonomes

Francesco Mondada

There is a large number of possible applications in the field of mobile robotics: Mail delivery robots, domestic or industrial vacuum cleaners, surveillance robots, demining robots and many others could be very interesting products. Despite this potential ...

EPFL1997

Handbook of Neural Computation

Many scientists and engineers now use neural networks to tackle problems that are either intractable, or unrealistically time consuming to solve, through traditional computational strategies. To address the need for speedy dissemination of new ideas in thi ...

Institute of Physics and Oxford University Press1996

A Boolean Approach to Construct Neural Networks for Non-Boolean Problems

A neural network construction method for problems specified for data sets with in- and/or output values in the continuous or discrete domain is described and evaluated. This approach is based on a Boolean approximation of the data set and is generic for va ...

1996