RT info:eu-repo/semantics/article
T1 Cognitive Neural Network Driving DoF-Scalable Limbs in Time-Evolving Situations
A1 Tapia, Carlos Calvo
A1 Villacorta-Atienza, Jose Antonio
A1 Kastalskiy, Innokentiy
A1 Diez-Hermano, Sergio
A1 Sanchez-Jimenez, Abel
A1 Makarov, Valeri A.
AB Object handling and manipulation are vital skills for humans and autonomous humanoid robots. The fundamental bases of how our brain solves such tasks remain largely unknown. Here we develop a novel approach that addresses the problem of limb movements in time-evolving situations at an abstract cognitive level. We exploit the concept of generalized cognitive maps constructed in the so-called handspace by a neural network simulating a wave simultaneously exploring different subject actions, independently on the number of objects in the workspace. We show that the approach is scalable to limbs with minimalistic and redundant numbers of degrees of freedom (DOF). It also allows biasing the effort of reaching a target among different DOF
PB IEEE
YR 2018
FD 2018
LK https://uvadoc.uva.es/handle/10324/83206
UL https://uvadoc.uva.es/handle/10324/83206
LA eng
NO International Joint Conference on Neural Networks, Octubre 2018
DS UVaDOC
RD 27-feb-2026