keywords: motor control

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Matarié, A.F.M.J., Jenkins, O.C. Automated Derivation of Primitives for Movement Classification International Conference on Humanoid Robotics 2000 : [pdf]

We present a new method for representing human movement compactly, in terms of a linear superimposition of simpler movements termed primitives. This method is a part of a larger research project aimed at modeling motor control and imitation using the notion of perceptuo-motor primitives, a basis set of coupled perceptual and motor routines. In our model, the perceptual system is biased by the set of motor behaviors the agent can execute, so it automatically classifies observed movements into its executable repertoire. In this paper, we describe a method for automatically deriving a set of primitives directly from human movement data. We used data from a psychophysical experiment on human imitation to derive a set of primitives, and then used those primitives as a basis for superposition and sequencing to reconstruct the original movements. We performed principal component analysis on segments from these data, resulting in a set of basis vectors. Next we clustered in the space of projections of segments onto the eigenvectors, to obtain a set of frequently used movements. To validate the approach experimentally, we used the movement obtained by expanding the cluster points in terms of the eigenvectors as a sequence of via points to control a humanoid dynamic simulation. We also developed an error metric to measure the effectiveness of the process.

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Harman, K.L., Humphrey, G.K., Goodale, M.A. Active manual control of object views facilitates visual recognition Current Biology 1999 (9):1315-1318

Gribble, P.L., Ostry, D.J., Sanguineti, V., Laboissiere, R. Are complex control signals required for human arm movement? Journal of Neurophysiology 1998 (79):1409-1424

Matarié, A.F.M.J., Jenkins, O.C. Automated Derivation of Primitives for Movement Classification International Conference on Humanoid Robotics 2000 :

Computational Motor Control

Mezic, i. Controllability of Hamiltonian Systems with Drift: Action-Angle variables and Ergodic Partition 2003 (3):2585- 2592

Bloch, A., Leonard, N.E., Marsden, J.E. Controlled Lagrangians and the stabilization of Mechanical Systems I: The First Matching Theorem IEEE Trans. on Systems and Control 2001 (45):2253-2270

Chen, T. Critical manifolds and stability in hamiltonian systems with non-holonomic constraints arXiv 2003

Kelly, S.D., Murray, R.M. Geometric phases and robotic locomotion 1994

Lie, W., Todorov, E., Pan, X. Hierarchical Optimal Control of Redundant biomechanical systems 2004

Luciani, A. Interaction as exchanged actions and their role in visual and auditory feedbacks 2004

Kawato, M. Internal models for motor control and trajectory planning Current Opinion in Neurobiology 1999 (9):718-727

Guigon, E. Interpolation and extrapolation in human behavior. Journal of Cognitive Neuroscience 2004 (16)3

Max Planck Institute for Biological Cybernetics

Klute, G.K., Czerniecki, J.M., Hannafor, B. McKibben Artificial Muscles: Pneumatic Actuators with Biomechanical Intelligence IEEE/ASME 1999 International Conference on Advanced Intelligent Mechatronics (AIM'99) 1999

Oriolo, G. Mobile robotics: control problems

Motor Control Laboratory, McGill

Motor Control Laboratory, U. Western Ontario

Bizzi, E., Tresch, M.C., Saltiel, P., d'Avella, A. New perspectives on spinal motor systems Nature Neuroscience 2000 (1):101-107

Kielau, G., Maisser, P. Nonholonomic multibody dynamics Multibody System Dynamics 2003 (9):213-236

Isidori, A. Nonlinear Control Systems 1995

Todorov, E., Jordan, M.I. Optimal feedback control as a theory of motor coordination Nature Neuroscience 2002 (5):11

Todorov, E. Optimality principles in sensorimotor control Nature Neuroscience 2004 (7)9:907-915

Wolpert, D.M., Ghahramani, Z., Flanagan, J.R. Perspectives and Problems in Motor Learning Trends in Cognitive Sciences 2001 (5)11:487-494

Paz, R. & al Preparatory activity in motor cortex reflects learning of local visuomotors skills Nature Neuroscience 2003 (6)8:882-890

Sensorimotor Control Lab, UCL

Harris, C.M., Wolpert, D.M. Signal-dependent noise determines motor planning Nature 1998 (394):780-784

Marsden, J.E., Ostrowski, J. Symmetries in Motion: Geometric Foundations of Motion Control Nonlinear Sci. Today 1998

Frisoli, A. The human-robot sensorimotor coupling: an engineering perspective 2004

Bloch, A. homepage

Fearing, R. homepage

Gribble, P.L. homepage

Leonard, N.E. homepage

Marsden, J.E. homepage

Murray, R.M. homepage

Oriolo, G. homepage

Ostry, D.J. homepage

Pandy, M. homepage

Simic, S. homepage

Stramigioli, S. homepage

Todorov, E. homepage

last computed Thu Dec 16 21:02:16 GMT+01:00 2004