A Computational Model for Rhythmic and Discrete Movements in Uni- and Bimanual Coordination

Ronsse, Renaud; Sternad, Dagmar; Lefèvre, Philippe

doi:10.1162/neco.2008.03-08-720

Cited by 47 publications

(41 citation statements)

References 77 publications

(183 reference statements)

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“…The third hypothesis assumes that rhythmic and discrete movements are two different (or partially different) classes of movements (Sternad et al, 2000;Buchanan et al, 2003). These hypotheses have been examined from behavioral (Buchanan et al, 2003;van Mourik and Beek, 2004), theoretical (Schöner, 1990;Huys et al, 2008;Ronsse et al, 2009), and neuronal (Spencer et al, 2003(Spencer et al, , 2007Schaal et al, 2004) perspectives. The current consensus is that rhythmic movements are not mere concatenations of discrete movements (i.e., the first hypothesis has been ruled out).…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Transfer of Visuomotor Learning between Discrete and Rhythmic Movements

Ikegami¹,

Hirashima²,

Taga³

et al. 2010

J. Neurosci.

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As long as we only focus on kinematics, rhythmic movement appears to be a concatenation of discrete movements or discrete movement appears to be a truncated rhythmic movement. However, whether or not the neural control processes of discrete and rhythmic movements are distinct has not yet been clearly understood. Here, we address this issue by examining the motor learning transfer between these two types of movements testing the hypothesis that distinct neural control processes should lead to distinct motor learning and transfer. First, we found that the adaptation to an altered visuomotor condition was almost fully transferred from the discrete out-andback movements to the rhythmic out-and-back movements; however, the transfer from the rhythmic to discrete movements was very small. Second, every time a new set of rhythmic movements was started, a considerable amount of movement error reappeared in the first and the following several cycles although the error converged to a small level by the end of each set. Last, we observed that when the discrete movement training was performed with intertrial intervals longer than 4 s, a significantly larger error appeared, specifically for the second and third cycles of the subsequent rhythmic movements, despite a seemingly full transfer to the first cycle. These results provide strong behavioral evidence that different neuronal control processes are involved in the two types of movements and that discrete control processes contribute to the generation of the first cycle of the rhythmic movement.

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Section: Introductionmentioning

confidence: 99%

Asymmetric Transfer of Visuomotor Learning between Discrete and Rhythmic Movements

Ikegami¹,

Hirashima²,

Taga³

et al. 2010

J. Neurosci.

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show abstract

“…Similarly, piano playing is regarded as an archetypal rhythmic action, yet simultaneously integrates discrete and very accurate reaches across the keyboard. Coordination and control of such hybrid actions may therefore pose more complex problems than each action by itself and probably engage overlapping circuitries and mechanisms (Ronsse et al 2009;Sternad 2008;Sternad et al 2000).…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of a Hybrid Rhythmic-Discrete Task: The Bouncing Ball Revisited

Ronsse

Wei

Sternad

2010

Journal of Neurophysiology

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Ronsse R, Wei K, Sternad D. Optimal control of a hybrid rhythmic-discrete task: the bouncing ball revisited. J Neurophysiol 103: 2482-2493, 2010. First published February 3, 2010 doi:10.1152/jn.00600.2009. Rhythmically bouncing a ball with a racket is a hybrid task that combines continuous rhythmic actuation of the racket with the control of discrete impact events between racket and ball. This study presents experimental data and a two-layered modeling framework that explicitly addresses the hybrid nature of control: a first discrete layer calculates the state to reach at impact and the second continuous layer smoothly drives the racket to this desired state, based on optimality principles. The testbed for this hybrid model is task performance at a range of increasingly slower tempos. When slowing the rhythm of the bouncing actions, the continuous cycles become separated into a sequence of discrete movements interspersed by dwell times and directed to achieve the desired impact. Analyses of human performance show increasing variability of performance measures with slower tempi, associated with a change in racket trajectories from approximately sinusoidal to less symmetrical velocity profiles. Matching results of model simulations give support to a hybrid control model based on optimality, and therefore suggest that optimality principles are applicable to the sensorimotor control of complex movements such as ball bouncing.

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“…Indeed, to the best of our knowledge, two main models for the simultaneous production of both discrete and rhythmic movements have been presented before, namely the models by De Rugy and Sternad (2003) and by Schaal et al (2000), and they have never been applied to robotic control. The model presented by De Rugy and Sternad (2003), later extended to bi-manual tasks by Ronsse et al (2009), is aimed at reproducing the key observations of the combination of discrete and rhythmic movements. It is based on a Matsuoka oscillator (Matsuoka (1985)) modeling the output of two coupled neurons, this output being transformed into a desired trajectory through the equation of the dynamics of the joint.…”

Section: Central Pattern Generatorsmentioning

confidence: 99%

Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives

2011

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Vertebrates are able to quickly adapt to new environments in a very robust, seemingly effortless way. To explain both this adaptivity and robustness, a very promising perspective in neurosciences is the modular approach to movement generation: Movements results from combinations of a finite set of stable motor primitives organized at the spinal level. In this article we apply this concept of modular generation of movements to the control of robots with a high number of degrees of freedom, an issue that is challenging notably because planning complex, multidimensional trajectories in time-varying environments is a laborious and costly process. We thus propose to decrease the complexity of the planning phase through the use of a combination of discrete and rhythmic motor primitives, leading to the decoupling of the planning phase (i.e. the choice of behavior) and the actual trajectory generation. Such implementation eases the control of, and the switch between, different behaviors by reducing the dimensionality of the high-level commands. Moreover, since the motor primitives are generated by dynamical systems, the trajectories can be smoothly modulated, either by high-level commands to change the current behavior or by sensory feedback information to adapt to environmental constraints. In order to show the generality of our approach, we apply the framework to interactive drumming and infant crawling in a humanoid robot. These experiments illustrate the simplicity of the control architecture in terms of planning, the integration of different types * This work was supported by the European Commission's Cognition Unit, projects RobotCub and AMARSi. S.G. is funded by a IST-EPFL grant.of feedback (vision and contact) and the capacity of autonomously switching between different behaviors (crawling and simple reaching).

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A Computational Model for Rhythmic and Discrete Movements in Uni- and Bimanual Coordination

Cited by 47 publications

References 77 publications

Asymmetric Transfer of Visuomotor Learning between Discrete and Rhythmic Movements

Asymmetric Transfer of Visuomotor Learning between Discrete and Rhythmic Movements

Optimal Control of a Hybrid Rhythmic-Discrete Task: The Bouncing Ball Revisited

Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives

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