Robots successfully manipulate objects in controlled environments. However, they fail in unknown environments. Few years old children lift and manipulate unfamiliar objects more dexterously than today's robots. Therefore, roboticists are looking for inspiration on neurophysiological studies to improve their robotics control models. We present an artificial intelligence control model for dexterous manipulation, and a grip and load force control algorithm, strongly inspired on neurophysiological studies of the human manipulation process. © Springer-Verlag Berlin Heidelberg 2005.
Documento: | Artículo |
Título: | Bio-inspired control of dexterous manipulation |
Autor: | Herrera, R.M.; Leoni, F. |
Ciudad: | Sydney |
Filiación: | Department of Computer Science, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina ARTS Lab., Scuola Superiore sant'Anna, Pisa, Italy Dapartamento de Computación, Ciudad Universitaria, Pabellón 1, 1428, Buenos Aires, Argentina |
Palabras clave: | Dexterous manipulation; Neural networks; Reinforcement learning; Robotics; Algorithms; Artificial intelligence; Computer control; Force control; Learning systems; Manipulators; Neural networks; Neurophysiology; Robotics; Robots; Artificial intelligence control model; Dexterous manipulation; Human manipulation process; Reinforcement learning; Biocontrol |
Año: | 2005 |
Volumen: | 3809 LNAI |
Página de inicio: | 1319 |
Página de fin: | 1322 |
DOI: | http://dx.doi.org/10.1007/11589990_195 |
Título revista: | 18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence |
Título revista abreviado: | Lect. Notes Comput. Sci. |
ISSN: | 03029743 |
Registro: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v3809LNAI_n_p1319_Herrera |