Abstract
On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.
Highlights
Humans regularly interact with objects with internal degrees of freedom from carrying a glass of water to using a cloth to polish a table
We show that the trajectories and velocity profiles we observed experimentally could be explained by a simple cost function and that the more realistic optimal control model captures aspects of the data that the point-mass model cannot explain
Simple tools have no internal degrees of freedom
Summary
Humans regularly interact with objects with internal degrees of freedom from carrying a glass of water to using a cloth to polish a table. While objects with no internal degrees of freedom can be regarded as a fixed extension of our limbs [1,2] non-rigid objects pose a more complex control problem. Stochastic optimal feedback control has emerged as a normative framework for human motor coordination [5,6,7]. We extended the optimal control framework to such object manipulation with internal degrees of freedom, in which both the position of the hand and the object need to be controlled. We show that the trajectories and velocity profiles we observed experimentally could be explained by a simple cost function and that the more realistic optimal control model captures aspects of the data that the point-mass model cannot explain. We tested our model on data from a previous study [13] and show that our optimal control model can account for the experimental data of a relatively simple massspring object
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