RT Conference Proceedings
T1 Cognitive Neural Network Driving DoF-Scalable Limbs in Time-Evolving Situations
A1 Calvo Tapia, Carlos
A1 Villacorta Atienza, José Antonio
A1 Kastalskiy, Innokentiy
A1 Díez Hermano, Sergio
A1 Sánchez Jiménez, Abel
A1 Makarov Slizneva, Valeriy
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.
SN 2161-4407
YR 2018
FD 2018
LK https://hdl.handle.net/20.500.14352/96303
UL https://hdl.handle.net/20.500.14352/96303
LA eng
NO C. C. Tapia, J. A. Villacorta-Atienza, I. Kastalskiy, S. Diez-Hermano, A. Sánchez-Jiménez and V. A. Makarov, "Cognitive Neural Network Driving DoF-Scalable Limbs in Time-Evolving Situations," 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018, pp. 1-7, doi: 10.1109/IJCNN.2018.8489562.
NO Ministerio de Economía y Competitividad (España)
DS Docta Complutense
RD 20 abr 2025