Using model knowledge for learning inverse dynamics

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2010, Pages 2677-2682

  Nguyen Tuong, Duy ^a   Peters, Jan ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN PROCESS REGRESSION; INVERSE DYNAMICS; LEARNING APPROACH; LEARNING MODELS; LEARNING PERFORMANCE; LEARNING SPEED; MEAN FUNCTIONS; MODEL APPROXIMATIONS; MODEL LEARNING; NON-PARAMETRIC; PHYSICAL MODEL; PHYSICS-BASED MODELING; PRIOR KNOWLEDGE; REGRESSION METHOD; RIGIDBODY DYNAMICS; ROBOT SYSTEM; SAMPLED DATA; SEMIPARAMETRIC; SEMIPARAMETRIC MODELS; STATE SPACE; TRACKING CONTROLS;

MODELS; REGRESSION ANALYSIS; ROBOTICS; ROBOTS;

DYNAMICS;

EID: 77955827116     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROBOT.2010.5509858     Document Type: Conference Paper

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