Multi-level cognitive machine-learning based concept for human-like "artificial" walking: application to autonomous stroll of humanoid robots

K. Madani 1 C. Sabourin 1
1 SYNAPSE
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
Abstract : We propose a machine-learning based multi-level cognitive model inspired from early-ages’ cognitive development of human’s locomotion skills for humanoid robot’s walking modeling. Contrary to the most of already introduced works dealing with biped robot’s walking modeling, which place the problem within the context of controlling specific kinds of biped robots, the proposed model attends to a global concept of biped walking ability’s construction independently from the robot to which the concept may be applied. The chief-benefit of the concept is that the issued machine-learning based structure takes advantage from “learning” capacity and “generalization” propensity of such models: allowing a precious potential to deal with high dimensionality, nonlinearity and empirical proprioceptive or exteroceptive information. Case studies and validation results are reported and discussed evaluating potential performances of the proposed approach.
Type de document :
Article dans une revue
Neurocomputing, Elsevier, 2011, 74, pp.1213-1228. 〈10.1016/j.neucom.2010.07.021〉
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01679984
Contributeur : Lab Lissi <>
Soumis le : mercredi 10 janvier 2018 - 12:24:09
Dernière modification le : vendredi 15 février 2019 - 18:23:24

Identifiants

Collections

Citation

K. Madani, C. Sabourin. Multi-level cognitive machine-learning based concept for human-like "artificial" walking: application to autonomous stroll of humanoid robots. Neurocomputing, Elsevier, 2011, 74, pp.1213-1228. 〈10.1016/j.neucom.2010.07.021〉. 〈hal-01679984〉

Partager

Métriques

Consultations de la notice

33