Symbolic Control with Biologically Inspired Motion Primitives

Outline of the Thesis This thesis is about the control of nonlinear dynamical systems with the superposition of a finite number of modules. Practically, admissible control actions are constrained to be within the space spanned by a finite number of elementary controls, called motion primitives. This control paradigm was inspired by recent experiments sheding a new light on the organization of sensory-motor structures in biological systems. The motivation of the thesis is twofold. First, we are interested in understanding how to reduce the complexity of planning movements in articulated structures with many degrees of freedom. Second, we want to understand if biology can be helpful in formulating a control paradigm for realistically simulating human movements. The thesis is organized into five different papers. All the papers make use of concepts and tools typical of system control theory. Paper A presents the motion primitives control paradigm and shows how to choose the modules so as to preserve the system controllability. Paper B deals with the problem of choosing the modules so as to compensate for parametric changes in the controlled system. Moreover, the problem of performing movements with arbitrary execution time is considered. Paper C proves some results on the minimum number of primitives necessary to preserve controllability. Paper D looks at the control paradigm from a different point of view and tries to answer questions which are related to the biologically inspired nature of the paradigm. Moreover, it gives solution the problem of optimally choosing the elementary modules to compensate for disturbances affecting the system. Finally, Paper E considers the problem of estimating the cost function that best approximates a given set of trajectories. The estimation problem is shown to be connected to the problem of realistically simulating human movements. Riassunto della Tesi In questa tesi si propone un paradigma di controllo innovativo. L’idea è quella di controllare un sistema dinamico nonlineare utilizzando la sovrapposizione di un numero finito di moduli di controllo. Sostanzialmente, l’insieme dei controlli ammissibili è costituito da uno spazio vettoriale generato da un numero finito di azioni elementari di controllo, chiamateprimitive del movimento . Questo paradigma di controllò e motivato da alcuni recenti esperimenti che hanno messo in luce un’organizzazione modulare dei sistemi senso-motori biologici. La motivazione della tesi è duplice. Da un lato ci interessa capire sè e possibile ridurre la complessit à della pianificazione dei

[1]  E Bizzi,et al.  Motor learning through the combination of primitives. , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[2]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[3]  Alex Pentland,et al.  Dynamic models of human motion , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[4]  B. Anderson,et al.  Optimal control: linear quadratic methods , 1990 .

[5]  Naomi Ehrich Leonard,et al.  Controllability and motion algorithms for underactuated Lagrangian systems on Lie groups , 2000, IEEE Trans. Autom. Control..

[6]  A. Jameson,et al.  Inverse Problem of Linear Optimal Control , 1973 .

[7]  E. Yaz Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[8]  Emanuel V. Todrov Studies of goal directed movements , 1998 .

[9]  M. Kawato,et al.  Formation and control of optimal trajectory in human multijoint arm movement , 1989, Biological Cybernetics.

[10]  Pattie Maes,et al.  Postural primitives: Interactive Behavior for a Humanoid Robot Arm , 1996 .

[11]  Pietro Perona,et al.  Decomposition of human motion into dynamics-based primitives with application to drawing tasks , 2003, Autom..

[12]  Ferdinando A. Mussa-Ivaldi,et al.  Vector summation of end-point impedance in kinematically redundant manipulators , 1993, Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93).

[13]  Oussama Khatib,et al.  A unified approach for motion and force control of robot manipulators: The operational space formulation , 1987, IEEE J. Robotics Autom..

[14]  Ruggero Frezza,et al.  Nonlinear control by a finite set of motion primitives , 2004 .

[15]  M. Egerstedt,et al.  Reconstruction of low-complexity control programs from data , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[16]  Ferdinando A Mussa-Ivaldi,et al.  Modular features of motor control and learning , 1999, Current Opinion in Neurobiology.

[17]  Yanxi Liu,et al.  Gait Sequence Analysis Using Frieze Patterns , 2002, ECCV.

[18]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[19]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation: Part I—Theory , 1985 .

[20]  Michael I. Jordan,et al.  An internal model for sensorimotor integration. , 1995, Science.

[21]  David C. Brogan,et al.  Animating human athletics , 1995, SIGGRAPH.

[22]  E. Bizzi,et al.  Characteristics of motor programs underlying arm movements in monkeys. , 1979, Journal of neurophysiology.

[23]  Jesse Hoey,et al.  Representation and recognition of complex human motion , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[24]  Charles R. Johnson,et al.  Topics in Matrix Analysis , 1991 .

[25]  M G Pandy,et al.  Static and dynamic optimization solutions for gait are practically equivalent. , 2001, Journal of biomechanics.

[26]  P. Morasso Spatial control of arm movements , 2004, Experimental Brain Research.

[27]  R. Frezza,et al.  Biologically inspired control of a kinematic chain using the superposition of motion primitives , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[28]  Michael J. Black,et al.  Parameterized Modeling and Recognition of Activities , 1999, Comput. Vis. Image Underst..

[29]  Dennis S. Bernstein,et al.  Finite-Time Stability of Continuous Autonomous Systems , 2000, SIAM J. Control. Optim..

[30]  Maja J. Mataric,et al.  Automated Derivation of Primitives for Movement Classification , 2000, Auton. Robots.

[31]  M. F.,et al.  Bibliography , 1985, Experimental Gerontology.

[32]  T D Sanger,et al.  Human Arm Movements Described by a Low-Dimensional Superposition of Principal Components , 2000, The Journal of Neuroscience.

[33]  Neville Hogan,et al.  Integrable Solutions of Kinematic Redundancy via Impedance Control , 1991, Int. J. Robotics Res..

[34]  Alberto Isidori,et al.  Nonlinear control systems: an introduction (2nd ed.) , 1989 .

[35]  Jean-Jacques E. Slotine,et al.  On Contraction Analysis for Non-linear Systems , 1998, Autom..

[36]  P. Krishnaprasad,et al.  Nonholonomic mechanical systems with symmetry , 1996 .

[37]  Christoph Bregler,et al.  Learning and recognizing human dynamics in video sequences , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[38]  James P. Ostrowski Computing reduced equations for robotic systems with constraints and symmetries , 1999, IEEE Trans. Robotics Autom..

[39]  J. Willems Least squares stationary optimal control and the algebraic Riccati equation , 1971 .

[40]  Stefano Soatto,et al.  Recognition of human gaits , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[41]  Richard M. Murray,et al.  A Mathematical Introduction to Robotic Manipulation , 1994 .

[42]  Ruggero Frezza,et al.  Control of a Manipulator with a Minimum Number of Motion Primitives , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[43]  Munther A. Dahleh,et al.  Maneuver-based motion planning for nonlinear systems with symmetries , 2005, IEEE Transactions on Robotics.

[44]  Jean-Jacques E. Slotine,et al.  Modular stability tools for distributed computation and control , 2003 .

[45]  Neville Hogan,et al.  The mechanics of multi-joint posture and movement control , 1985, Biological Cybernetics.

[46]  F. A. Mussa-lvaldi,et al.  Convergent force fields organized in the frog's spinal cord , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[47]  W. Boothby An introduction to differentiable manifolds and Riemannian geometry , 1975 .

[48]  H. Zelaznik,et al.  Motor-output variability: a theory for the accuracy of rapid motor acts. , 1979, Psychological review.

[49]  Jitendra Malik,et al.  Tracking people with twists and exponential maps , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[50]  Naomi Ehrich Leonard,et al.  Motion Primitives for Stabilization and Control of Underactuated Vehicles , 1998 .

[51]  E. Bizzi,et al.  Motor-space coding in the central nervous system. , 1990, Cold Spring Harbor symposia on quantitative biology.

[52]  Antonio Bicchi,et al.  Steering driftless nonholonomic systems by control quanta , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[53]  G. Marro,et al.  Employing the algebraic Riccati equation for the solution of the finite-horizon LQ problem , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[54]  V. Haimo Finite time controllers , 1986 .

[55]  J. Coron On the stabilization in finite time of locally controllable systems by means of continuous time-vary , 1995 .

[56]  E. Bizzi,et al.  Responses to spinal microstimulation in the chronically spinalized rat and their relationship to spinal systems activated by low threshold cutaneous stimulation , 1999, Experimental Brain Research.

[57]  R. E. Kalman,et al.  When Is a Linear Control System Optimal , 1964 .

[58]  B. Molinari The stable regulator problem and its inverse , 1973 .

[59]  A. Jameson,et al.  Optimality of linear control systems , 1972 .

[60]  Jon Rigelsford,et al.  Modelling and Control of Robot Manipulators , 2000 .

[61]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[62]  F A Mussa-Ivaldi,et al.  Computations underlying the execution of movement: a biological perspective. , 1991, Science.

[63]  Jitendra Malik,et al.  Learning Appearance Based Models: Mixtures of Second Moment Experts , 1996, NIPS.

[64]  M. Latash Neurophysiological basis of movement , 1998 .

[65]  Jun Wang,et al.  Obstacle avoidance for kinematically redundant manipulators using a dual neural network , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[66]  Claude Samson,et al.  Robot Control: The Task Function Approach , 1991 .

[67]  Ferdinando A. Mussa-Ivaldi,et al.  Vector field approximation: a computational paradigm for motor control and learning , 1992, Biological Cybernetics.

[68]  Ferdinando A. Mussa-Ivaldi,et al.  Nonlinear force fields: a distributed system of control primitives for representing and learning movements , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[69]  E. Bizzi,et al.  Linear combinations of primitives in vertebrate motor control. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[70]  Yoshiyuki Tanaka,et al.  Bio-mimetic trajectory generation of robots via artificial potential field with time base generator , 2002, IEEE Trans. Syst. Man Cybern. Part C.