Search results for " Imitation learning"

Imitation Learning and Anchoring through Conceptual Spaces

2007

In order to have a robotic system able to effectively learn by imitation and not merely reproduce the movements of a human teacher, the system should have the capability to deeply understand the perceived actions to be imitated. This paper deals with the development of a cognitive architecture for learning by imitation in which a rich conceptual representation of the observed actions is built. The purpose of the following discussion is to show how the same conceptual representation can be used both in a bottom-up approach, in order to learn sequences of actions by imitation learning paradigm, and in a top-down approach, in order to anchor the symbolical representations to the perceptual act…

A Cognitive Framework for Imitation Learning

2006

Abstract In order to have a robotic system able to effectively learn by imitation, and not merely reproduce the movements of a human teacher, the system should have the capabilities of deeply understanding the perceived actions to be imitated. This paper deals with the development of cognitive architecture for learning by imitation in which a rich conceptual representation of the observed actions is built. The purpose of the following discussion is to show how this Conceptual Area can be employed to efficiently organize perceptual data, to learn movement primitives from human demonstration and to generate complex actions by combining and sequencing simpler ones. The proposed architecture ha…

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

2003

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with an human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator.

An adaptive probabilistic approach to goal-level imitation learning

2010

Imitation learning has been recognized as a promising technique to teach robots advanced skills. It is based on the idea that robots could learn new behaviors by observing and imitating the behaviors of other skilled actors. We propose an adaptive probabilistic graphical model which copes with three core issues of any imitative behavior: observation, representation and reproduction of skills. Our model, Growing Hierarchical Dynamic Bayesian Network (GHDBN), is hierarchical (i.e. able to characterize structured behaviors at different levels of abstraction), and growing (i.e. skills are learned or updated incrementally - and at each level of abstraction - every time a new observation sequence…

An adaptive probabilistic graphical model for representing skills in PbD settings

2010

Anchoring by Imitation Learning in Conceptual Spaces

2005

In order to have a robotic system able to effectively learn by imitation, and not merely reproduce the movements of a human teacher, the system should have the capabilities of deeply understanding the perceived actions to be imitated. This paper deals with the development of a cognitive architecture for learning by imitation in which a rich conceptual representation of the observed actions is built. The purpose of the following discussion is to show how the same conceptual representation can be used both in a bottom-up approach, in order to learn sequences of actions by imitation learning paradigm, and in a top-down approach, in order to anchor the symbolical representations to the perceptu…