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For the purpose of motor coordination, the nervous system faces a complex control problem, involving redundant degrees of freedom, nonlinear dynamics of limbs and actuators, noise, and perturbations. Models and architectures have been proposed to describe motor coordination in terms of computational processes, and identify possible simplifying strategies that would alleviate the workload of the nervous system. Here, we review several strategies ranging from biomechanical to function levels. We conclude that none of the proposed strategies actually tackle the overall problem of motor coordination. Then we present a principled approach that provides an overarching account to motor control. Chhabra M, Jacobs RA (2006) Near-optimal human adaptive control across different noise environments. J Neurosci 26(42):10883-10887. Chiel H, Beer RD (1997) The brain has a body: Adaptive behavior emerges from interactions of nervous system, body, and environment. Trends Neurosci 20(12):553-557. Conditt MA, Mussa-Ivaldi FA (1999) Central representation of time during motor learning. Proc Natl Acad Sci USA 96(20):11625-11630. d'Avella A, Saltiel P, Bizzi E (2003) Combinations of muscle synergies in the construction of a natural motor behavior. Nat Neurosci 6(3):300-308. Demer JL (2006) Current concepts of mechanical and neural factors in ocular motility. Curr Opin Neurol 19(1):4-13. Desmurget M, Grafton S (2000) Forward modeling allows feedback control for fast reaching movements. Trends Cogn Sci 4(11):423-431. Dornay M, Mussa-Ivaldi FA, McIntyre J, Bizzi E (1993) Stability constraints for the distributed control of motor behavior. Neural Netw 6(9):1045-1059. Engelbrecht SE, Berthier NE, O'Sullivan LP (2003) The undershoot bias: Learning to act optimally under uncertainty. Psychol Sci 14(3):257-261. Engelbrecht SE, Fernandez JP (1997) Invariant characteristics of horizontal-plane minimum-torque-change movements with one mechanical degree of freedom. Biol Cybern 76(5):321-329. Feldman AG, Levin MF (1995) The origin and use of positional frames of reference in motor control. Behav Brain Sci 18(4):723-744. Fitts PM (1954) The information capacity of the human motor system in controlling the amplitude of movement. J Exp Psychol 47(6):381-391. Flanagan JR, Lolley S (2001) The inertial anisotropy of the arm is accurately predicted during movement planning. J Neurosci 21(4):1361-1369. Flanagan JR, Ostry DJ, Feldman AG (1993) Control of trajectory modifications in target-directed reaching. J Mot Behav 25(3):140-152. Flash T (1987) The control of hand equilibrium trajectories in multi-joint arm movements. Biol Cybern 57(4-5):257-274. Flash T, Henis E (1991) Arm trajectory modifications during reaching towards visual targets. J Cogn Neurosci 3(3):220-230. Flash T, Hogan N (1985) The coordination of arm movements: An experimentally confirmed mathematical model. J Neurosci 5(7):1688-1703. Forget R, Lamarre Y (1987) Rapid elbow flexion in the absence of proprioceptive and cutaneous feedback. Hum Neurobiol 6(1):27-37.
For the purpose of motor coordination, the nervous system faces a complex control problem, involving redundant degrees of freedom, nonlinear dynamics of limbs and actuators, noise, and perturbations. Models and architectures have been proposed to describe motor coordination in terms of computational processes, and identify possible simplifying strategies that would alleviate the workload of the nervous system. Here, we review several strategies ranging from biomechanical to function levels. We conclude that none of the proposed strategies actually tackle the overall problem of motor coordination. Then we present a principled approach that provides an overarching account to motor control. Chhabra M, Jacobs RA (2006) Near-optimal human adaptive control across different noise environments. J Neurosci 26(42):10883-10887. Chiel H, Beer RD (1997) The brain has a body: Adaptive behavior emerges from interactions of nervous system, body, and environment. Trends Neurosci 20(12):553-557. Conditt MA, Mussa-Ivaldi FA (1999) Central representation of time during motor learning. Proc Natl Acad Sci USA 96(20):11625-11630. d'Avella A, Saltiel P, Bizzi E (2003) Combinations of muscle synergies in the construction of a natural motor behavior. Nat Neurosci 6(3):300-308. Demer JL (2006) Current concepts of mechanical and neural factors in ocular motility. Curr Opin Neurol 19(1):4-13. Desmurget M, Grafton S (2000) Forward modeling allows feedback control for fast reaching movements. Trends Cogn Sci 4(11):423-431. Dornay M, Mussa-Ivaldi FA, McIntyre J, Bizzi E (1993) Stability constraints for the distributed control of motor behavior. Neural Netw 6(9):1045-1059. Engelbrecht SE, Berthier NE, O'Sullivan LP (2003) The undershoot bias: Learning to act optimally under uncertainty. Psychol Sci 14(3):257-261. Engelbrecht SE, Fernandez JP (1997) Invariant characteristics of horizontal-plane minimum-torque-change movements with one mechanical degree of freedom. Biol Cybern 76(5):321-329. Feldman AG, Levin MF (1995) The origin and use of positional frames of reference in motor control. Behav Brain Sci 18(4):723-744. Fitts PM (1954) The information capacity of the human motor system in controlling the amplitude of movement. J Exp Psychol 47(6):381-391. Flanagan JR, Lolley S (2001) The inertial anisotropy of the arm is accurately predicted during movement planning. J Neurosci 21(4):1361-1369. Flanagan JR, Ostry DJ, Feldman AG (1993) Control of trajectory modifications in target-directed reaching. J Mot Behav 25(3):140-152. Flash T (1987) The control of hand equilibrium trajectories in multi-joint arm movements. Biol Cybern 57(4-5):257-274. Flash T, Henis E (1991) Arm trajectory modifications during reaching towards visual targets. J Cogn Neurosci 3(3):220-230. Flash T, Hogan N (1985) The coordination of arm movements: An experimentally confirmed mathematical model. J Neurosci 5(7):1688-1703. Forget R, Lamarre Y (1987) Rapid elbow flexion in the absence of proprioceptive and cutaneous feedback. Hum Neurobiol 6(1):27-37.
Online optimal planning of robotic arm movement is addressed. Optimality is inspired by computational models, where a "cost function" is used to describe limb motions according to different criteria. A method is proposed to implement optimal planning in Cartesian space, minimizing some cost function, by means of numerical approximation to a generalized nonlinear model predictive control problem. The Extended RItz Method is applied as a functional approximation technique. Differently from other approaches, the proposed technique can be applied on platforms with strict control temporal constraints and limited processing capability, since the computational burden is completely concentrated in an off-line phase. The trajectory generation on-line is therefore computationally efficient. Task to joint space conversion is implemented on-line by a closed loop inverse kinematics algorithm, taking into account the robot's physical limits. Experimental results, where a 4DOF arm moves according to a particular nonlinear cost, show the effectiveness of the proposed approach, and suggest interesting future developments.
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