Optimal control predicts human performance on objects with internal degrees of freedom.

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.

Motor learning.

Although learning a motor skill, such as a tennis stroke, feels like a unitary experience, researchers who study motor control and learning break the processes involved into a number of interacting components. These components can be organized into four main groups. First, skilled performance requires the effective and efficient gathering of sensory information, such as deciding where and when to direct one's gaze around the court, and thus an important component of skill acquisition involves learning how best to extract task-relevant information. Second, the performer must learn key features of the task such as the geometry and mechanics of the tennis racket and ball, the properties of the court surface, and how the wind affects the ball's flight. Third, the player needs to set up different classes of control that include predictive and reactive control mechanisms that generate appropriate motor commands to achieve the task goals, as well as compliance control that specifies, for example, the stiffness with which the arm holds the racket. Finally, the successful performer can learn higher-level skills such as anticipating and countering the opponent's strategy and making effective decisions about shot selection. In this Primer we shall consider these components of motor learning using as an example how we learn to play tennis.

A microstructural investigation of deformation in fine SiC particulate- and whisker-reinforced aluminium-matrix composites

Aluminium-based composites, reinforced with low volume fractions of whiskers and small particles, have been formed by a powder route. The materials have been tested in tension, and the microstructures examined using transmission electron microscopy. The whisker composites showed an improvement in flow stress over the particulate composites, and this was linked to an initially enhanced work-hardening rate in the whisker composites. The overall dislocation densities were estimated to be somewhat higher in the whisker composites than the particulate composites, but in the early stages of deformation the distribution was rather different, with deformation in the whisker material being far more localized and inhomogeneous. This factor, together with differences in the internal stress distribution in the materials, is used to explain the difference in mechanical properties.

Fine scale structures from deformation of aluminium containing small alumina particles

Microstructures and mechanical properties have been studied in aluminium containing a fine dispersion of alumina particles, deformed by cold-rolling to strains between 1.4 and 3.5. The microstructure was characterised by TEM. The deformation structures evolved very rapidly, forming a nanostructured material, with fine subgrains about 0.2 μm in diameter and a fraction of high-angle boundaries which was already high at a strain of 1.4, but continued to increase with rolling strain. The yield stress and ductility of the rolled materials were measured in tension, and properties were similar for all materials. Yield stress measurements were correlated with estimates made using microstructural models. The role of small particles in forming and stabilising the deformation structure is discussed. This nanostructured cold-deformed alloy has mechanical properties which are usefully enhanced at comparatively low cost. This gives it, and similar particle-strengthened alloys, good potential for commercial exploitation. © 2002 Acta Materialia Inc. Published by Elsevier Science Ltd. All rights reserved.

Coarse-to-fine vision-based localization by indexing scale-invariant features

This paper presents a novel coarse-to-fine global localization approach inspired by object recognition and text retrieval techniques. Harris-Laplace interest points characterized by scale-invariant transformation feature descriptors are used as natural landmarks. They are indexed into two databases: a location vector space model (LVSM) and a location database. The localization process consists of two stages: coarse localization and fine localization. Coarse localization from the LVSM is fast, but not accurate enough, whereas localization from the location database using a voting algorithm is relatively slow, but more accurate. The integration of coarse and fine stages makes fast and reliable localization possible. If necessary, the localization result can be verified by epipolar geometry between the representative view in the database and the view to be localized. In addition, the localization system recovers the position of the camera by essential matrix decomposition. The localization system has been tested in indoor and outdoor environments. The results show that our approach is efficient and reliable. © 2006 IEEE.