Impedance control is selectively tuned to multiple directions of movement

Kadiallah, Abdelhamid; Liaw, Gary; Kawato, Mitsuo; Franklin, David W.; Burdet, Etienne

doi:10.1152/jn.00079.2011

Cited by 28 publications

(20 citation statements)

References 77 publications

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“…There have been a number of experimental studies demonstrating a control of endpoint stiffness orientation well beyond that shown to be possible in our simulations (Burdet et al 2001;Franklin et al 2007;Kadiallah et al 2011). All of these experimental studies differed from our simulations in one important respect, which is that they considered stiffness control during movement rather than the postural conditions considered in our simulations.…”

Section: Discussionmentioning

confidence: 69%

Biomechanical constraints on the feedforward regulation of endpoint stiffness

Murray

Perreault

2012

Journal of Neurophysiology

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Although many daily tasks tend to destabilize arm posture, it is still possible to have stable interactions with the environment by regulating the multijoint mechanics of the arm in a task-appropriate manner. For postural tasks, this regulation involves the appropriate control of endpoint stiffness, which represents the stiffness of the arm at the hand. Although experimental studies have been used to evaluate endpoint stiffness control, including the orientation of maximal stiffness, the underlying neural strategies remain unknown. Specifically, the relative importance of feedforward and feedback mechanisms has yet to be determined due to the difficulty separately identifying the contributions of these mechanisms in human experiments. This study used a previously validated three-dimensional musculoskeletal model of the arm to quantify the degree to which the orientation of maximal endpoint stiffness could be changed using only steady-state muscle activations, used to represent feedforward motor commands. Our hypothesis was that the feedforward control of endpoint stiffness orientation would be significantly constrained by the biomechanical properties of the musculoskeletal system. Our results supported this hypothesis, demonstrating substantial biomechanical constraints on the ability to regulate endpoint stiffness throughout the workspace. The ability to regulate stiffness orientation was further constrained by additional task requirements, such as the need to support the arm against gravity or exert forces on the environment. Together, these results bound the degree to which slowly varying feedforward motor commands can be used to regulate the orientation of maximum arm stiffness and provide a context for better understanding conditions in which feedback control may be needed.

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Section: Discussionmentioning

confidence: 69%

Biomechanical constraints on the feedforward regulation of endpoint stiffness

Murray

Perreault

2012

Journal of Neurophysiology

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“…This is produced by using a radial basis function network with local activation fields. Simulations systematically test whether the model reproduces experimental results, and revisit representative psychophysical studies of generalization in motor learning [2], [3], [24]. We simulate generalization to a variety of movements with distinct dynamics [2], and investigate learning for both fine grained force fields [3] and multiple movements with lateral instability [24].…”

Section: Introductionmentioning

confidence: 96%

Generalization in Adaptation to Stable and Unstable Dynamics

2012

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Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization.

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“…The learning algorithm was therefore extended to multiple movements by generalization the model over the state space using a radial basis neural network mapping [32]. This extended model was able to replicate adaptation [33] and generalization [34] to a variety of stable dynamics as well as adapt simultaneously to instability in two different directions of movement [35]. This computational model of adaptation, relying on a simple update rule is able to explain and predict the changes in endpoint stiffness, force and muscle activation, learning to coordinate control of redundant muscles to perform a task while minimizing instability, energy and systematic error.…”

Section: Learning Impedance Controlmentioning

confidence: 99%

Impedance control: Learning stability in human sensorimotor control

Franklin

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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The human sensorimotor control system generates movement by adapting and controlling the mechanics of the musculoskeletal system. To generate skilful movements the sensorimotor control system must be able to predict and compensate for any disturbances generated either in our own body or in the external environment. While stable and repeatable perturbations can be easily adapted through iterative learning, instability and unpredictability require a different approach: impedance control. Here I outline the arguments for impedance control as a fundamental process of human adaptation as well as describe evidence suggesting the manner in which such impedance can be learned in order to ensure the stability of the neuro-mechanical system.

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Impedance control is selectively tuned to multiple directions of movement

Cited by 28 publications

References 77 publications

Biomechanical constraints on the feedforward regulation of endpoint stiffness

Biomechanical constraints on the feedforward regulation of endpoint stiffness

Generalization in Adaptation to Stable and Unstable Dynamics

Impedance control: Learning stability in human sensorimotor control

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