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Literature DB >> 16271466

Computational motor control in humans and robots.

Abstract

Computational models can provide useful guidance in the design of behavioral and neurophysiological experiments and in the interpretation of complex, high dimensional biological data. Because many problems faced by the primate brain in the control of movement have parallels in robotic motor control, models and algorithms from robotics research provide useful inspiration, baseline performance, and sometimes direct analogs for neuroscience.

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Year: 2005 PMID： 16271466 DOI： 10.1016/j.conb.2005.10.009

Source DB: PubMed Journal: Curr Opin Neurobiol ISSN： 0959-4388 Impact factor: 6.627

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Cited

17 in total

1. Linear hypergeneralization of learned dynamics across movement speeds reveals anisotropic, gain-encoding primitives for motor adaptation.

Authors: Wilsaan M Joiner; Obafunso Ajayi; Gary C Sing; Maurice A Smith
Journal: J Neurophysiol Date: 2010-09-29 Impact factor: 2.714

2. Control of bimanual rhythmic movements: trading efficiency for robustness depending on the context.

Authors: Renaud Ronsse; Jean-Louis Thonnard; Philippe Lefèvre; Rodolphe Sepulchre
Journal: Exp Brain Res Date: 2008-02-14 Impact factor: 1.972

3. Optimality, stochasticity, and variability in motor behavior.

Authors: Emmanuel Guigon; Pierre Baraduc; Michel Desmurget
Journal: J Comput Neurosci Date: 2007-05-22 Impact factor: 1.621

4. Naturalistic arm movements during obstacle avoidance in 3D and the identification of movement primitives.

Authors: Britta Grimme; John Lipinski; Gregor Schöner
Journal: Exp Brain Res Date: 2012-08-23 Impact factor: 1.972

Review 5. Spinal cord modularity: evolution, development, and optimization and the possible relevance to low back pain in man.

Authors: Simon F Giszter; Corey B Hart; Sheri P Silfies
Journal: Exp Brain Res Date: 2009-10-09 Impact factor: 1.972

Computational motor control in humans and robots.

1. Linear hypergeneralization of learned dynamics across movement speeds reveals anisotropic, gain-encoding primitives for motor adaptation.

2. Control of bimanual rhythmic movements: trading efficiency for robustness depending on the context.

3. Optimality, stochasticity, and variability in motor behavior.

4. Naturalistic arm movements during obstacle avoidance in 3D and the identification of movement primitives.

Review 5. Spinal cord modularity: evolution, development, and optimization and the possible relevance to low back pain in man.

6. Unsupervised learning of reflexive and action-based affordances to model adaptive navigational behavior.

7. The New Robotics-towards human-centered machines.

8. Rate-dependent control strategies stabilize limb forces during human locomotion.

9. Estimating the sources of motor errors for adaptation and generalization.

10. Bayesian integration and non-linear feedback control in a full-body motor task.