Name
Title | ||
|---|---|---|
Learning to recognize objects through curiosity-driven manipulation with the iCub humanoid robot |
Abstract | ||
|---|---|---|
In this paper we address the problem of learning to recognize objects by manipulation in a developmental robotics scenario. In a life-long learning perspective, a humanoid robot should be capable of improving its knowledge of objects with active perception. Our approach stems from the cognitive development of infants, exploiting active curiosity-driven manipulation to improve perceptual learning of objects. These functionalities are implemented as perception, control and active exploration modules as part of the Cognitive Architecture of the MACSi project. In this paper we integrate these functionalities into an active perception system which learns to recognise objects through manipulation. Our work in this paper integrates a bottom-up vision system, a control system of a complex robot system and a top-down interactive exploration method, which actively chooses an exploration method to collect data and whether interacting with humans is profitable or not. Experimental results show that the humanoid robot iCub can learn to recognize 3D objects by manipulation and in interaction with teachers by choosing the adequate exploration strategy to enhance competence progress and by focusing its efforts on the most complex tasks. Thus the learner can learn interactively with humans by actively self-regulating its requests for help. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/DevLrn.2013.6652525 | Development and Learning and Epigenetic Robotics |
Keywords | Field | DocType |
cognition,continuing professional development,humanoid robots,learning (artificial intelligence),object recognition,3d objects,macsi project,active curiosity-driven manipulation,active perception system,bottom-up vision system,cognitive architecture,cognitive development,complex robot system,control system,developmental robotics scenario,humanoid robot,icub,infants,life-long learning perspective,perceptual object learning,top-down interactive exploration method,learning artificial intelligence | Robot learning,Computer vision,iCub,Active perception,Learning theory,Computer science,Perceptual learning,Developmental robotics,Human–computer interaction,Artificial intelligence,Cognitive architecture,Humanoid robot | Conference |
Citations | PageRank | References |
6 | 0.43 | 23 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| sao mai nguyen | 1 | 6 | 2.80 |
| Serena Ivaldi | 2 | 163 | 19.72 |
| Natalia Lyubova | 3 | 29 | 2.58 |
| Alain Droniou | 4 | 61 | 3.71 |
| Damien Gerardeaux-Viret | 5 | 9 | 0.82 |
| David Filliat | 6 | 646 | 47.26 |
| vincent padois | 7 | 168 | 15.26 |
| Olivier Sigaud | 8 | 539 | 53.35 |
| Pierre-yves Oudeyer | 9 | 1209 | 104.05 |