Internal models for motor control.

By Mitsuo Kawato, D. M. Wolpert. Novartis Foundation symposium, 2007

336 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/40219ab988910dbef9791c1ffce7ce7ec1a245e4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Jan Peters
2. Stefan Schaal
3. Auke Jan Ijspeert
4. Ferdinando A. Mussa-Ivaldi
5. Jens Kober
6. Emilio Bizzi
7. Sergey Levine
8. Gerhard Neumann

In Cognitive Neuroscience

1. Stefan Schaal
2. Auke Jan Ijspeert
3. Jan Peters
4. Ferdinando A. Mussa-Ivaldi
5. Emilio Bizzi
6. Jens Kober
7. Aude Gemma Billard
8. Peter Pastor

In Control and Systems Engineering

1. Auke Jan Ijspeert
2. Stefan Schaal
3. Jan Peters
4. Ferdinando A. Mussa-Ivaldi
5. Sergey Levine
6. Pieter Abbeel
7. Gerhard Neumann
8. Aude Gemma Billard

Citations in the corpus

• Motor learning through the combination of primitives. (2000, background): “…' refers to two distinct mathematical transformations : ( i ) the transformation from a motor command to the consequent behaviour , and ( ii ) the trans - formation from a desired behaviour to the corresp onding motor command ( Jordan & Rumelhart 1992 ; Kawato & Wolpert 1998 ; McIntyre et al .”
• Dynamics systems vs. optimal control--a unifying view. (2007, background): “The most common priors are that the control policy can be reduced to a desired trajectory, 1⁄2xdðtÞ; _ xdðtÞ : Optimal control or RL approaches for trajectory learning are computationally significantly more tractable (Kawato and Wolpert, 1998; Peters et al., 2005).”