Model learning with local Gaussian process regression

Advanced Robotics

Volumn 23, Issue 15, 2009, Pages 2015-2034

  Nguyen Tuong, Duy ^a   Seeger, Matthias ^a   Peters, Jan ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

Inverse dynamics; Machine learning; Model based control; Nonparametric regression; Robotics

Indexed keywords

ACTUATOR DYNAMICS; COMPETITIVE LEARNING; CONTROL PERFORMANCE; ENERGY EFFICIENT; FLEXIBLE FRAMEWORK; GAUSSIAN PROCESS REGRESSION; HIGH DIMENSIONAL DATA; INVERSE DYNAMICS; LOCAL APPROXIMATION; LOCALLY WEIGHTED PROJECTION REGRESSIONS; MACHINE LEARNING; MEASURED DATA; MODEL LEARNING; MODEL-BASED CONTROL; NON-LINEARITY; NONPARAMETRIC REGRESSION; ONLINE LEARNING; PARAMETRIC MODELS; REAL-TIME LEARNING; REAL-TIME MODELS; REGRESSION METHOD; REGRESSION TECHNIQUES; RIGID BODY; ROBOT ARMS; ROBOT CONTROLS; ROBOT INVERSE DYNAMICS; ROBOT MODEL; SUPPORT VECTOR REGRESSIONS;

CONTROL NONLINEARITIES; DYNAMICS; E-LEARNING; EDUCATION; ENERGY EFFICIENCY; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); HYDRAULIC MACHINERY; INTERNET; LARGE SCALE SYSTEMS; MACHINE DESIGN; RADAR ANTENNAS; ROBOT LEARNING; ROBOTICS; ROBOTS;

REGRESSION ANALYSIS;

EID: 70449580824     PISSN: 01691864     EISSN: 15685535     Source Type: Journal    
DOI: 10.1163/016918609X12529286896877     Document Type: Conference Paper

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