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Active search for Bifurcations

arXiv.org Artificial Intelligence

What in dynamical systems is called a bifurcation, in a laboratory setting (or in nature) is perceived as a qualitative change in the long-term observed dynamic behavior, sometimes dramatic. Pinpointing the location of these phenomena in state parameter space, and deciphering the nature of the underlying transitions, has been the focus of significant scientific effort for decades, e.g. in Biology [21, 17, 26, 54, 62, 32, 15]) or Chemistry [1, 48, 18, 76, 11, 46, 37]. In fact, accurate location of bifurcation points remains an active field of research computationally and experimentally [3]. When a reliable mathematical model is available, one can locate bifurcations either analytically (if the model is simple enough) or through scientific computing, e.g. in the context of numerical continuation. Such approaches reduce to the numerical solution of a system of (deterministic) equations that characterize bifurcations of a certain type [19, 13, 41].


Global Regularization of Inverse Kinematics for Redundant Manipulators

Neural Information Processing Systems

When m n, we say that the manipulator has redundant degrees--of -freedom (dot). The inverse kinematics problem is the following: given a desired workspace location x, find joint variables 0 such that f(O) x. Even when the forward kinematics is known, 255 256 DeMers and Kreutz-Delgado the inverse kinematics for a manipulator is not generically solvable in closed form (Craig. 1986).


Global Regularization of Inverse Kinematics for Redundant Manipulators

Neural Information Processing Systems

When m n, we say that the manipulator has redundant degrees--of -freedom (dot). The inverse kinematics problem is the following: given a desired workspace location x, find joint variables 0 such that f(O) x. Even when the forward kinematics is known, 255 256 DeMers and Kreutz-Delgado the inverse kinematics for a manipulator is not generically solvable in closed form (Craig. 1986).


Global Regularization of Inverse Kinematics for Redundant Manipulators

Neural Information Processing Systems

When m n, we say that the manipulator has redundant degrees--of-freedom (dot). The inverse kinematics problem is the following: given a desired workspace location x, find joint variables 0 such that f(O) x. Even when the forward kinematics is known, 255 256 DeMers and Kreutz-Delgado the inverse kinematics for a manipulator is not generically solvable in closed form (Craig. 1986).


Learning Global Direct Inverse Kinematics

Neural Information Processing Systems

S n, the robot has redundant degrees-of-freedom (dof's). In general, control objectives such as the positioning and orienting of the endeffector are specified with respect to task space coordinates; however, the manipulator is typica1ly controlled only in the configuration space.


Learning Global Direct Inverse Kinematics

Neural Information Processing Systems

S n, the robot has redundant degrees-of-freedom (dof's). In general, control objectives such as the positioning and orienting of the endeffector are specified with respect to task space coordinates; however, the manipulator is typica1ly controlled only in the configuration space.


Learning Global Direct Inverse Kinematics

Neural Information Processing Systems

In general, control objectives such as the positioning and orienting of the endeffector arespecified with respect to task space coordinates; however, the manipulator is typica1ly controlled only in the configuration space.