Concurrent adaptation of force and impedance in the redundant muscle system

Tee, Keng Peng; Franklin, David W.; Kawato, Mitsuo; Milner, Theodore E.; Burdet, Etienne

doi:10.1007/s00422-009-0348-z

Cited by 101 publications

(88 citation statements)

References 49 publications

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“…Many models have considered stiffness regulation only at the joint level (Flash and Mussa-Ivaldi 1990;Gomi and Osu 1998;Tee et al 2004). Those models that have directly incorporated muscles (Osu and Gomi 1999;Shin et al 2009;Tee et al 2010) have considered only a reduced muscle set and constant moment arms, at times selected for computational simplicity or to fit best the experimental data. Although these assumptions have been sufficient for the intended purposes, they may not readily allow for generalization to novel situations, especially given our finding that stiffness estimates are most sensitive to the geometric properties of the model.…”

Section: Discussionmentioning

confidence: 99%

Muscle short-range stiffness can be used to estimate the endpoint stiffness of the human arm

Murray

Perreault

2011

Journal of Neurophysiology

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Hu X, Murray WM, Perreault EJ. Muscle short-range stiffness can be used to estimate the endpoint stiffness of the human arm. J Neurophysiol 105: 1633-1641, 2011. First published February 2, 2011 doi:10.1152/jn.00537.2010.-The mechanical properties of the human arm are regulated to maintain stability across many tasks. The static mechanics of the arm can be characterized by estimates of endpoint stiffness, considered especially relevant for the maintenance of posture. At a fixed posture, endpoint stiffness can be regulated by changes in muscle activation, but which activation-dependent muscle properties contribute to this global measure of limb mechanics remains unclear. We evaluated the role of muscle properties in the regulation of endpoint stiffness by incorporating scalable models of muscle stiffness into a three-dimensional musculoskeletal model of the human arm. Two classes of muscle models were tested: one characterizing short-range stiffness and two estimating stiffness from the slope of the force-length curve. All models were compared with previously collected experimental data describing how endpoint stiffness varies with changes in voluntary force. Importantly, muscle properties were not fit to the experimental data but scaled only by the geometry of individual muscles in the model. We found that forcedependent variations in endpoint stiffness were accurately described by the short-range stiffness of active arm muscles. Over the wide range of evaluated arm postures and voluntary forces, the musculoskeletal model incorporating short-range stiffness accounted for 98 Ϯ 2, 91 Ϯ 4, and 82 Ϯ 12% of the variance in stiffness orientation, shape, and area, respectively, across all simulated subjects. In contrast, estimates based on muscle force-length curves were less accurate in all measures, especially stiffness area. These results suggest that muscle short-range stiffness is a major contributor to endpoint stiffness of the human arm. Furthermore, the developed model provides an important tool for assessing how the nervous system may regulate endpoint stiffness via changes in muscle activation. musculoskeletal model WE COMMONLY USE OUR HANDS to move and manipulate objects in different environments. Many of these tasks tend to destabilize arm posture (Rancourt and Hogan 2001). Nevertheless, they can be completed because the central nervous system regulates the mechanical properties of the arm to compensate for these instabilities, usually ensuring that the coupled system of the arm and its environment remains stable so that posture can be maintained (McIntyre et al. 1996). Understanding how this regulation occurs and the relative contributions of the nervous system and the intrinsic biomechanics of the arm remains an important problem in motor control.

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Section: Discussionmentioning

confidence: 99%

Muscle short-range stiffness can be used to estimate the endpoint stiffness of the human arm

Murray

Perreault

2011

Journal of Neurophysiology

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“…Repetitive reaching and pointing has been examined by a number of authors including Shadmehr and Mussa-Ivaldi (1994) (see also Shadmehr and Mussa-Ivaldi (2012)), Burdet, Tee, Mareels, Milner, Chew, Franklin, Osu, and Kawato (2006) and Tee, Franklin, Kawato, Milner, and Burdet (2010). An iterative learning control explanation of these results is given by Zhou, Oetomo, Tan, Burdet, and Mareels (2012).…”

Section: Examples: Adaptive Human Reachingmentioning

confidence: 99%

Event-Based Data Acquisition and Reconstruction—Mathematical Background

Thao¹

2018

Event-Based Control and Signal Processing

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Intermittent control has a long history in the physiological literature and there is strong experimental evidence that some human control systems are intermittent. Intermittent control has also appeared in various forms in the engineering literature. This article discusses a particular mathematical model of Event-driven Intermittent Control which brings together engineering and physiological insights and builds on and extends previous work in this area. Illustrative examples of the properties of Intermittent Control in a physiological context are given together with suggestions for future research directions in both physiology and engineering. i arXiv:1407.3543v1 [q-bio.QM]

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“…A lot can be learned from biological systems, where numerous studies report that humans change the stiffness of their arms not only between different tasks but also during the execution of a task [30][31][32]. Several related robot controllers have been proposed.…”

Section: Learning Varying Stiffness Controlmentioning

confidence: 99%

Learning motions from demonstrations and rewards with time-invariant dynamical systems based policies

Rey

Kronander

Farshidian³

et al. 2017

Auton Robot

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An important challenge when using Reinforcement Learning for learning motions in robotics is the choice of parameterization for the policy. We use Gaussian Mixture Regression to extract a parameterization with relevant non-linear features from a set of demonstrations of a motion following the paradigm of Learning from Demonstration. The resulting parameterization takes the form of a non-linear time-invariant dynamical system (DS). We use this time-invariant DS as a parameterized policy for a variant of the PI 2 policy search algorithm. This paper contributes by adapting PI 2 for our time-invariant motion representation. We introduce two novel parameter exploration schemes that can be used to 1) sample model parameters to achieve a uniform exploration in state space and 2) explore while ensuring stability of the resulting motion model. Additionally, a state dependent stiffness profile is learned simultaneously to the reference trajectory and both are used together in a variable impedance control architecture. This learning architecture is validated in a hardware experiment consisting of a digging task using a KUKA LWR platform.

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Concurrent adaptation of force and impedance in the redundant muscle system

Cited by 101 publications

References 49 publications

Muscle short-range stiffness can be used to estimate the endpoint stiffness of the human arm

Muscle short-range stiffness can be used to estimate the endpoint stiffness of the human arm

Event-Based Data Acquisition and Reconstruction—Mathematical Background

Learning motions from demonstrations and rewards with time-invariant dynamical systems based policies

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