Andrea Maesani

Learn More
Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but also in finding the largest number of different solutions satisfying minimal requirements. However,(More)
Programmable self-assembly of chained robotic systems holds potential for the automatic construction of complex robots from a minimal set of building blocks. However, current robotic platforms are limited to modules of uniform rigidity, which results in a limited range of obtainable morphologies and thus functionalities of the system. To address these(More)
Viability Evolution is an abstraction of artificial evolution which operates by eliminating candidate solutions that do not satisfy viability criteria. Viability criteria are defined as boundaries on the values of objectives and constraints of the problem being solved. By adapting these boundaries it is possible to drive the search towards desired regions(More)
Modular or multi-cellular robots hold the promise to adapt their morphology to task and environment. However, research in modular robotics has traditionally been limited to mechanically non-adaptive systems due to hard building blocks and rigid connection mechanisms. To improve adaptation and global flexibility, we suggest the use of modules made of soft(More)
The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can(More)
The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adaptation Evolution Strategy is one of the most efficient algorithms for unconstrained optimization problems, it cannot be readily applied to constrained ones. Here, we used concepts from(More)
Predicting the structure of large molecular assemblies remains a challenging task in structural biology when using integrative modeling approaches. One of the main issues stems from the treatment of heterogeneous experimental data used to predict the architecture of native complexes. We propose a new method, applied here for the first time to a set of(More)
OBJECTIVE To evaluate the effects of electrically assisted movement therapy (EAMT) in which patients use functional electrical stimulation, modulated by a custom device controlled through the patient's unaffected hand, to produce or assist task-specific upper limb movements, which enables them to engage in intensive goal-oriented training. DESIGN(More)