Formation and control of optimal trajectory in human multijoint arm movement : minimum torque-change model

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.

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.

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.

TL;DR: This goal is to demonstrate how specific models emerging from the computational approach provide a theoretical framework for movement neuroscience.

TL;DR: In motor control, mapping high-level goals to low-level motor commands often leads to ambiguities, as there are usually many ways a task can be achieved.

TL;DR: A change in thumb velocity in the downward direction when TMS was applied, indicating that tissue excitation elicited by TMS causes loss of control of peripheral muscle motor control during voluntary movement.

TL;DR: The research presented in this book was the first to systematically examine the forces exerted by five digits on a hand-held object duriung static force and torque production tasks and addresses the strategies used by the central nervous system to fulfill the apparently conflicting force and Torque production requirements.

TL;DR: This paper presents techniques for cooperation and task execution of an anthropomorphous two-armed robot with two seven degrees of freedom arms and an inverse kinematic solver is proposed to find all joint angles for given postures of two effectors on both arms.