Computational approaches to motor control

Flash, Tamar; Sejnowski, Terrence J.

doi:10.1016/s0959-4388(01)00265-3

Cited by 95 publications

(59 citation statements)

References 78 publications

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“…One can also draw an interesting parallel of our work with the equilibrium hypothesis in neuromotor control (Flash & Sejnowski, 2001;Latash, 1993). By rearranging equation 2.1 as follows,…”

Section: Neural Control Of Movementmentioning

confidence: 82%

Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors

et al. 2013

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Nonlinear dynamical systems have been used in many disciplines to model complex behaviors, including biological motor control, robotics, perception, economics, traffic prediction, and neuroscience. While often the unexpected emergent behavior of nonlinear systems is the focus of investigations, it is of equal importance to create goal-directed behavior (e.g., stable locomotion from a system of coupled oscillators under perceptual guidance). Modeling goal-directed behavior with nonlinear systems is, however, rather difficult due to the parameter sensitivity of these systems, their complex phase transitions in response to subtle parameter changes, and the difficulty of analyzing and predicting their long-term behavior; intuition and time-consuming parameter tuning play a major role. This letter presents and reviews dynamical movement primitives, a line of research for modeling attractor behaviors of autonomous nonlinear dynamical systems with the help of statistical learning techniques. The essence of our approach is to start with a simple dynamical system, such as a set of linear differential equations, and transform those into a weakly nonlinear system with prescribed attractor dynamics by means of a learnable autonomous forcing term. Both point attractors and limit cycle attractors of almost arbitrary complexity can be generated. We explain the design principle of our approach and evaluate its properties in several example applications in motor control and robotics.

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“…One can also draw an interesting parallel of our work with the equilibrium hypothesis in neuromotor control (Flash & Sejnowski, 2001;Latash, 1993). By rearranging equation 2.1 as follows,…”

Section: Neural Control Of Movementmentioning

confidence: 82%

Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors

et al. 2013

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“…The processes by which animals choose simple paths are not well understood (Hooper 2006). It has been proposed (Sabes 2000;Wolpert and Ghahramani 2000;Flash and Sejnowski 2001) that those paths are chosen that balance motor command amplitude and endpoint tracking, leading to smooth and direct paths involving minimal motor commands and endpoint error.…”

Section: Path Planningmentioning

confidence: 99%

Soft Robotics: Biological Inspiration, State of the Art, and Future Research

Trivedi

Rahn

Kier

et al. 2008

Applied Bionics and Biomechanics

1,191

536

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Traditional robots have rigid underlying structures that limit their ability to interact with their environment. For example, conventional robot manipulators have rigid links and can manipulate objects using only their specialised end effectors. These robots often encounter difficulties operating in unstructured and highly congested environments. A variety of animals and plants exhibit complex movement with soft structures devoid of rigid components. Muscular hydrostats (e.g. octopus arms and elephant trunks) are almost entirely composed of muscle and connective tissue and plant cells can change shape when pressurised by osmosis. Researchers have been inspired by biology to design and build soft robots. With a soft structure and redundant degrees of freedom, these robots can be used for delicate tasks in cluttered and/or unstructured environments. This paper discusses the novel capabilities of soft robots, describes examples from nature that provide biological inspiration, surveys the state of the art and outlines existing challenges in soft robot design, modelling, fabrication and control.

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“…It has been suggested that the computations underlying the control of reaching differentiate between kinematics and dynamics (Atkeson, 1989;Flash and Sejnowski, 2001;Shadmehr and Wise, 2004). First, the visual difference vector between the hand and the target is translated into a kinematic plan, i.e., a plan of how the joint angles have to change to bring the hand to the target.…”

Section: Experiments 2: Kinematic Versus Dynamic Execution Errorsmentioning

confidence: 99%

Neural Correlates of Reach Errors

et al. 2005

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Reach errors may be broadly classified into errors arising from unpredictable changes in target location, called target errors, and errors arising from miscalibration of internal models (e.g., when prisms alter visual feedback or a force field alters limb dynamics), called execution errors. Execution errors may be caused by miscalibration of dynamics (e.g., when a force field alters limb dynamics) or by miscalibration of kinematics (e.g., when prisms alter visual feedback). Although all types of errors lead to similar on-line corrections, we found that the motor system showed strong trial-by-trial adaptation in response to random execution errors but not in response to random target errors. We used functional magnetic resonance imaging and a compatible robot to study brain regions involved in processing each kind of error. Both kinematic and dynamic execution errors activated regions along the central and the postcentral sulci and in lobules V, VI, and VIII of the cerebellum, making these areas possible sites of plastic changes in internal models for reaching. Only activity related to kinematic errors extended into parietal area 5. These results are inconsistent with the idea that kinematics and dynamics of reaching are computed in separate neural entities. In contrast, only target errors caused increased activity in the striatum and the posterior superior parietal lobule. The cerebellum and motor cortex were as strongly activated as with execution errors. These findings indicate a neural and behavioral dissociation between errors that lead to switching of behavioral goals and errors that lead to adaptation of internal models of limb dynamics and kinematics.

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Computational approaches to motor control

Cited by 95 publications

References 78 publications

Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors

Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors

Soft Robotics: Biological Inspiration, State of the Art, and Future Research

Neural Correlates of Reach Errors

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