Open AccessDissertation
Formation and control of optimal trajectory in human multijoint arm movement : minimum torque-change model
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The article was published on 1988-01-01 and is currently open access. It has received 551 citations till now.read more
Citations
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Proceedings ArticleDOI
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel,Andrew Y. Ng +1 more
TL;DR: This work thinks of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and gives an algorithm for learning the task demonstrated by the expert, based on using "inverse reinforcement learning" to try to recover the unknown reward function.
Journal ArticleDOI
Optimal feedback control as a theory of motor coordination.
TL;DR: This work shows that the optimal strategy in the face of uncertainty is to allow variability in redundant (task-irrelevant) dimensions, and proposes an alternative theory based on stochastic optimal feedback control, which emerges naturally from this framework.
Journal ArticleDOI
Internal models for motor control and trajectory planning
TL;DR: The 'minimum variance model' is another major recent advance in the computational theory of motor control, strongly suggesting that both kinematic and dynamic internal models are utilized in movement planning and control.
Journal ArticleDOI
Signal-dependent noise determines motor planning
Chris Harris,Daniel M. Wolpert +1 more
TL;DR: This theory provides a simple and powerful unifying perspective for both eye and arm movement control and accurately predicts the trajectories of both saccades and arm movements and the speed–accuracy trade-off described by Fitt's law.
Journal ArticleDOI
Computational principles of movement neuroscience
TL;DR: This goal is to demonstrate how specific models emerging from the computational approach provide a theoretical framework for movement neuroscience.
References
More filters
Proceedings ArticleDOI
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel,Andrew Y. Ng +1 more
TL;DR: This work thinks of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and gives an algorithm for learning the task demonstrated by the expert, based on using "inverse reinforcement learning" to try to recover the unknown reward function.
Journal ArticleDOI
Optimal feedback control as a theory of motor coordination.
TL;DR: This work shows that the optimal strategy in the face of uncertainty is to allow variability in redundant (task-irrelevant) dimensions, and proposes an alternative theory based on stochastic optimal feedback control, which emerges naturally from this framework.
Journal ArticleDOI
Internal models for motor control and trajectory planning
TL;DR: The 'minimum variance model' is another major recent advance in the computational theory of motor control, strongly suggesting that both kinematic and dynamic internal models are utilized in movement planning and control.
Journal ArticleDOI
Signal-dependent noise determines motor planning
Chris Harris,Daniel M. Wolpert +1 more
TL;DR: This theory provides a simple and powerful unifying perspective for both eye and arm movement control and accurately predicts the trajectories of both saccades and arm movements and the speed–accuracy trade-off described by Fitt's law.
Journal ArticleDOI
Computational principles of movement neuroscience
TL;DR: This goal is to demonstrate how specific models emerging from the computational approach provide a theoretical framework for movement neuroscience.
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