Speeding up the learning of robot kinematics through function decomposition

IEEE Transactions on Neural Networks

Volumn 16, Issue 6, 2005, Pages 1504-1512

  de Angulo, Vicente Ruiz ^a   Torras, Carme ^a  

^a INSTITUT DE ROBÒTICA I INFORMÀTICA INDUSTRIAL CSIC UPC   (Spain)

Author keywords

Function approximation; Learning inverse kinematics; Parameterized self organizing map (PSOM); Robot kinematics; Training samples

Indexed keywords

APPROXIMATION THEORY; CONFORMAL MAPPING; INDUSTRIAL ROBOTS; INVERSE KINEMATICS; LEARNING ALGORITHMS; POLYNOMIALS; SELF ORGANIZING MAPS;

FUNCTION APPROXIMATION; FUNCTION DECOMPOSITION; PARAMETERIZED SELF-ORGANIZING MAP (PSOM); ROBOT KINEMATICS; TRAINING SAMPLES;

ROBOT LEARNING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOMECHANICS; COMPUTER SIMULATION; METHODOLOGY; MOVEMENT (PHYSIOLOGY); ROBOTICS; THEORETICAL MODEL; TIME;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMECHANICS; COMPUTER SIMULATION; MODELS, THEORETICAL; MOVEMENT; ROBOTICS; TIME FACTORS;

EID: 28244479767     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2005.852970     Document Type: Article

References (11)

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