Name
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 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 activities of the robotic system. Experiments concerned with the problem of teaching a humanoid robotic system simple manipulative tasks are reported. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1080/08839510701252619 | Applied Artificial Intelligence |
Keywords | Field | DocType |
conceptual spaces,cognitive architecture,symbolical representation,imitation learning,rich conceptual representation,top-down approach,robotic system,conceptual representation,following discussion,bottom-up approach,human teacher,humanoid robotic system,bottom up,humanoid robot,top down | Robotic systems,Computer science,Cognitive science,Anchoring,Cognitive imitation,Artificial intelligence,Imitation,Cognitive architecture,Imitation learning,Perception,Machine learning | Journal |
Volume | Issue | ISSN |
21 | 4-5 | 0883-9514 |
Citations | PageRank | References |
8 | 0.60 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Chella | 1 | 374 | 65.74 |
| Haris Dindo | 2 | 125 | 17.49 |
| Ignazio Infantino | 3 | 151 | 32.13 |