Abstract
Recent experiments on frogs and rats, have led to the hypothesis that sensory-motor systems are organized into a finite number of linearly combinable modules; each module generates a motor command that drives the system to a predefined equilibrium. Surprisingly, in spite of the infiniteness of different movements that can be realized, there seems to be only a handful of these modules. The structure can be thought of as a vocabulary of "elementary control actions". Admissible controls, which in principle belong to an infinite dimensional space, are reduced to the linear vector space spanned by these elementary controls. In the present paper we address some theoretical questions that arise naturally once a similar structure is applied to the control of nonlinear kinematic chains. First of all, we show how to choose the modules so that the system does not loose its capability of generating a "complete" set of movements. Secondly, we realize a "complete" vocabulary with a minimal number of elementary control actions. Subsequently, we show how to modify the control scheme so as to compensate for parametric changes in the system to be controlled. Remarkably, we construct a set of modules with the property of being invariant with respect to the parameters that model the growth of an individual. Robustness against uncertainties is also considered showing how to optimally choose the modules equilibria so as to compensate for errors affecting the system. Finally, the motion primitive paradigm is extended to locomotion and a related formalization of internal (proprioceptive) and external (exteroceptive) variables is given.
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References
Bienenstock E, Geman S (1993) Compositionality in neural systems. Arbib, M.A. (ed), The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge pp 223–226
Bloch AM, Krishnaprasad PS, Marsden JE, Murray RM (1996) Nonholonomic Mechanical Systems with Symmetry. Archive for Rational Mechanics and Analysis 136(1):21–99
Boothby William M (2002) An Introduction to Differentiable Manifolds and Riemannian Geometry. second edn. Academic Press.
Bullo F, Leonard NE, Lewis, AD (1998) Controllability and Motion Algorithms for Underactuated Lagrangian Systems on Lie Groups. IEEE Transactions on Automatic Control 45(8):1437–1454
Del Vecchio D, Murray, RM, Perona P (2003) Decomposition of human motion into dynamics-based primitives with application to drawing tasks. Automatica 39:2085–2098
Fornasini E, Marchesini G (1994) Appunti di Teoria dei Sistemi. Edizioni Libreria Progetto. In Italian.
Flash T, Hogan N (1985) The Coordination of Arm Movements: An Experimentally Confirmed Mathematical Model. The Journal of Neuroscience 5:1688–1703
Frazzoli E (2004) Maneuver-Based Motion Planning for Nonlinear Systems with Symmetries. Submitted to IEEE Transactions on Robotics and Automation.
Gandolfo F, Mussa-Ivaldi, FA (1993) Vector summation of end-point impedance in kinematically redundant manipulators. of: Proceedings of IEEE/RJS International Conference of Intelligent Robots and Systems. IROS93 pp 1627–1634
Giszter SF, Mussa-Ivaldi FA, Bizzi E (1993) Convergent Force Fields Organized in the Frog's Spinal Cord. The Journal of Neuroscience 13(2):467–491
Hogan N (1985a) Impedance control: An approach to manipulation: part I - theory, part II - implementation, part III - applications. Trans. ASME Journal of Dynamics, Systems, Measurement, and Control 107:1–24
Hogan N (1985b) The mechanics of multi-joint posture and movementcontrol. Biological Cybernetics 52:315–331
Isidori A (1995) Nonlinear Control Systems: Third Edition. Springer
Khatib O (1987) A Unified Approach for Motion and Force Control of Robot Manipulators: The Operational Space Formulation. IEEE Journal of Robotics and Automation RA-3(1):43–53
Lewis FL, Syrmos VL (1995) Optimal control. Jhon Wiley and Sons, Inc.
Marigo A, BicchiA (1998) Steering driftless nonholonomic systems by control quanta. In: Proceedings of 1998 Conference on Decision and Control
Morasso P (1981) Spatial Control of Arm Movements. Experimental Brain Research 42(2):223–7
Murray RM, Li Z, Sastry SS (1994) A Mathematical Introduction to Robotic Manipulation. CRC Press.
Mussa-Ivaldi FA (1997) Nonlinear Force Fields: A Distributed System of Control Promitives for Representing and Learning Movements. In: CIRA ’97, Montrey California, USA
Mussa-Ivaldi FA (1999) Modular features of motor control and learning. Current Opinion in Neurobiology 9:713–717
Mussa-Ivaldi FA, Bizzi E (2000) Motor Learning through the Combination of Primitives. Philosophical Transactions of the Royal Society: Biological Sciences 355:1755–1769
Mussa-Ivaldi FA, Hogan N (1991) Integrable Solutions Of Kinematic Redundancy Via Impedance Control. The International Journal of Robotics Research 10(5):481–491
Mussa-Ivaldi FA, Giszter SF, Bizzi E (1994) Linear combinations of primitives in vertebrate motor control. Proc. Natl. Acad. Sci. USA 91:7534–7538
Mussa-Ivaldi FA, Giszter SF (1992) Vector Field Approximation: A computational paradigm for motor control and learning. Biological Cybernetics 37:491–500
Nori F, Frezza R (2004a) Biologically Inspired Control of a Kinematic Chain Using the Superposition of Motion Primitives. In: Proceedings of 2004 Conference on Decision and Control
Nori F, Frezza R (2004b) Nonlinear Control by a Finite Set of Motion Primitives. In: Proceedings of 2004 Nonlinear Control Systems Design. IFAC
Ostrowski J (1999) Computing Reduced Equations for Robotic Systems with Constraints and Symmetries. IEEE Transactions on Robotics and Automation 15(1):111–123
Samson C, Espiau Bs, Borgne M.Le 1991. Robot control: the Task Function Approach. Oxford University Press, New york
Sanger TD (2000) Human Arm Movements Described by a Low-dimensional Superposition of Principal Components. The Journal of Neuroscience 20(3):1066–1072
Sciavicco L, Siciliano B (2000) Modelling and Control of Robot Manipulators. second edn. Springer
Shadmehr R, Mussa-Ivaldi FA (1994) Adaptive Representation of Dynamics During Learning of a Motor Task. Journal of Neuroscience 5(14):3208–3224
Slotine JE, Li W (1991) Applied NonLinear Control. Prentice-Hall International Eds.
Slotine JJ (2003) Modular stability tools for distributed computation and control. International Journal of Adaptive Control and Signal Processing 17(6):397–416
Todorov E, Jordan M (2002) Optimal feedback control as a theory of motor coordination. Nature Neuroscience 5(11):1226–1235
Poggio T, Smale S (2003) The mathematics of learning: dealing with data. Notices of the American Mathrmatical Society 50(5):537–544
Uno Y, Kawato M, Suzuki R (1989) Formation and control of optimal trajectory in human multijoint arm movement. Biological Cybernetics 61(2):89–101
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Nori, F., Frezza, R. A control theory approach to the analysis and synthesis of the experimentally observed motion primitives. Biol Cybern 93, 323–342 (2005). https://doi.org/10.1007/s00422-005-0008-x
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DOI: https://doi.org/10.1007/s00422-005-0008-x