In this paper, we show how a simulated humanoid robot controlled by an artificial neural network can acquire the ability to manipulate spherical objects located over a table by reaching, grasping, and lifting them. The robot controller is developed through an adaptive process in which the free parameters encode the control rules that regulate the fine-grained interaction between the agent and the environment, and the variations of these free parameters are retained or discarded on the basis of their effects at the level of the behavior exhibited by the agent. The robot develops the sensory-motor coordination required to carry out the task in two different conditions; that is, with or without receiving as input a linguistic instruction that ...
In this paper we present a neuro-robotic model that uses artificial neural networks for investigatin...
Abstract—The current research analyses and demonstrates how spoken language can be used by human use...
Abstract—Populations of simulated agents controlled by dy-namical neural networks are trained by art...
In this paper, we show how a simulated humanoid robot controlled by an artificial neural network can...
Abstract–In this paper, we show how a simulated humanoid robot controlled by an artificial neural ne...
One of the biggest questions in modern electrical and computer engineering is that of computational ...
Building intelligent systems with human level competence is the ultimate grand challenge for science...
In this paper, we show how a simulated anthropomorphic robotic arm controlled by an artificial neura...
International audienceOne of the principal functions of human language is to allow people to coordin...
Building intelligent systems with human level of competence is the ultimate grand challenge for scie...
In this paper, we propose a language grounding method that relates verbs which imply body manipulati...
Abstract — This paper describes a developmental sequence that allows a humanoid robot to learn about...
This paper presents a cognitive learning system for robot recognition and composite action learning....
This paper describes a developmental approach to the design of a humanoid robot. The robot, equipped...
Learning by instruction allows humans programming a robot to achieve a task using spoken language, w...
In this paper we present a neuro-robotic model that uses artificial neural networks for investigatin...
Abstract—The current research analyses and demonstrates how spoken language can be used by human use...
Abstract—Populations of simulated agents controlled by dy-namical neural networks are trained by art...
In this paper, we show how a simulated humanoid robot controlled by an artificial neural network can...
Abstract–In this paper, we show how a simulated humanoid robot controlled by an artificial neural ne...
One of the biggest questions in modern electrical and computer engineering is that of computational ...
Building intelligent systems with human level competence is the ultimate grand challenge for science...
In this paper, we show how a simulated anthropomorphic robotic arm controlled by an artificial neura...
International audienceOne of the principal functions of human language is to allow people to coordin...
Building intelligent systems with human level of competence is the ultimate grand challenge for scie...
In this paper, we propose a language grounding method that relates verbs which imply body manipulati...
Abstract — This paper describes a developmental sequence that allows a humanoid robot to learn about...
This paper presents a cognitive learning system for robot recognition and composite action learning....
This paper describes a developmental approach to the design of a humanoid robot. The robot, equipped...
Learning by instruction allows humans programming a robot to achieve a task using spoken language, w...
In this paper we present a neuro-robotic model that uses artificial neural networks for investigatin...
Abstract—The current research analyses and demonstrates how spoken language can be used by human use...
Abstract—Populations of simulated agents controlled by dy-namical neural networks are trained by art...