Symbolic Control with Biologically Inspired Motion Primitives
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[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.