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On the existence of stationary optimal receding-horizon strategies for dynamic teams with common past information structures.
We study the problem of detecting and localizing objects in still, gray-scale images making use of the part-based representation provided by non-negative matrix factorizations. Non-negative matrix factoriza-tion represents an emerging example of subspace methods which is able to extract interpretable parts from a set of template image objects and then to… (More)
Nonnegative matrix factorizations (NMF) have recently assumed an important role in several fields, such as pattern recognition, automated image exploitation, data clustering and so on. They represent a peculiar tool adopted to obtain a reduced representation of multivariate data by using additive components only, in order to learn parts-based… (More)
This paper deals with the problem of planning and controlling the trajectories of the legs of a mobile robot moving through statically stable conngurations. Legs are supposed to possess signiicant weight compared to the chassis, and a degree of redundancy that can be used to maximize the robustness of equilibrium. We use optimal control techniques for… (More)
A new integrated approach to the global control problem of the locomotion of non-holonomic robots was developed, motivated by several observations of the functional and performance requirements. Its main features are: • The organization of the global motion into elementary modalities together with a properly designed event-driven feedback enables to… (More)
Redundancy and concurrency in motor control are basic problems for multi-joint, multi-limb robots. The viscous-electric properties of pseudo-muscular actuators provide an organising factor because they define equilibrium postures that influence body movements as 'postural attractors'. The authors report the state of advancement of a pseudo-muscular actuator… (More)
Linear dimensionality reduction techniques are powerful tools for image analysis as they allow the identification of important features in a data set. In particular, nonnegative matrix factorization (NMF) has become very popular as it is able to extract sparse, localized and easily interpretable features by imposing an additive combination of nonnegative… (More)