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We propose a model predictive control (MPC) scheme for the real-time optimization of interactions between logistic traffic and urban mobility, in contexts where a port is located within a city center. The aim is to mitigate negative phenomena related to traffic sharing the same urban space, through the efficient operation of existing infrastructure at a tactical/operational level. To this purpose, we first define an integrated model of the port/city interactions, focusing on the import and export chains in proximity of the port where trucks and general mobility vehicles interact. Then, we implement an MPC approach in order to optimize an objective function reflecting the interests of the different involved stakeholders. An important feature of the presented model is that it allows the analysis of different levels of cooperation between the city and the port, highlighting possible advantages of increased coordination at the operational level. To evaluate the effect of the proposed control scheme, we present a case study based on the port of Genova in north-west Italy.

We have developed an imitation learning approach for the image-based control of a low-cost low-accuracy robot arm. The image-based control of manipulation arms is still an unsolved problem, at least under challenging conditions such as those here addressed. Many attempts for solutions in the literature are based on machine learning, generally relying on deep neural network architectures. In typical imitation approaches, the deep network learns from a human expert. In our case the network is trained on state/action pairs obtained through a Belief Space Planning algorithm, a stochastic method that requires only a rough tuning, particularly suited to unstructured and dynamic environments. Our approach allows to obtain a lightweight manipulation system that demonstrated its efficiency, robustness and good performance in real-world tests, and that is reproducible in experiments and results, despite its inaccuracy and non-repeatable kinematics. The proposed system performs well on a simple reaching task, requiring limited training on our quite challenging platform. The main contribution of the proposed work lies in the definition and real-world testing of an efficient controller, based on the integration of Belief Space Planning with the imitation learning paradigm, that enables even inaccurate, very low-cost robotic manipulators to be actually controlled and employed in the field.

In this paper, we describe a preliminary experiment of citizenship engagement in the context of marine robotics using imitation learning to train a controller that mimics human behavior. The experiment has been carried out during the Festival della Comunicazione in Camogli, Italy, in September 2019. In more detail, citizens have been asked to pilot a small, light, and safe autonomous surface vehicle in front of a crowded public beach with the goal of performing an S-shaped path. The trajectories and controls performed by non-expert human operators have been recorded with the aim of training an imitation system that, after collecting a sufficient number of trajectory-control pairs, has been able to drive the vehicle without human intervention. To learn the human behavior, echo state networks have been employed as approximating architectures. The resulting controller turned out to be very effective in successfully performing the considered experiment with a reduced amount of training trajectories by imitating the human behavior also in unknown situations. The success of this experiment may pave the way to new research processes where citizens are actively engaged.

The need for extracting a small sample from a large amount of real data, possibly streaming, arises routinely in learning problems, e.g., for storage, to cope with computational limitations, obtain good training/test/validation sets, and select minibatches for stochastic gradient neural network training. Unless we have reasons to select the samples in an active way dictated by the specific task and/or model at hand, it is important that the distribution of the selected points is as similar as possible to the original data. This is obvious for unsupervised learning problems, where the goal is to gain insights on the distribution of the data, but it is also relevant for supervised problems, where the theory explains how the training set distribution influences the generalization error. In this paper, we analyze the technique of stratified sampling from the point of view of distances between probabilities. This allows us to introduce an algorithm, based on recursive binary partition of the input space, aimed at obtaining samples that are distributed as much as possible as the original data. A theoretical analysis is proposed, proving the (greedy) optimality of the procedure together with explicit error bounds. An adaptive version of the algorithm is also introduced to cope with streaming data. Simulation tests on various data sets and different learning tasks are also provided.

The traditional extreme learning machine (ELM) approach is based on a random assignment of the hidden weight values, while the linear coefficients of the output layer are determined analytically. This brief presents an analysis based on geometric properties of the sampling points used to assign the weight values, investigating the replacement of random generation of such values with low-discrepancy sequences (LDSs). Such sequences are a family of sampling methods commonly employed for numerical integration, yielding a more efficient covering of multidimensional sets with respect to random sequences, without the need for any computationally intensive procedure. In particular, we prove that the universal approximation property of the ELM is guaranteed when LDSs are employed, and how an efficient covering affects the convergence positively. Furthermore, since LDSs are generated deterministically, the results do not have a probabilistic nature. Simulation results confirm, in practice, the good theoretical properties given by the combination of ELM with LDSs.

Local linear regression models, a kind of nonparametric structures that locally perform a linear estimation of the target function, are analyzed in the context of empirical risk minimization (ERM) for function learning. The analysis is carried out with emphasis on geometric properties of the available data. In particular, the discrepancy of the observation points used both to build the local regression models and compute the empirical risk is considered. This allows to treat indifferently the caseg in which the samples come from a random external source and the one in which the input space can be freely explored. Both consistency of the ERM procedure and approximating capabilities of the estimator are analyzed, proving conditions to ensure convergence. Since the theoretical analysis shows that the estimation improves as the discrepancy of the observation points becomes smaller, low-discrepancy sequences, a family of sampling methods commonly employed for efficient numerical integration, are also analyzed. Simulation results involving two different examples of function learning are provided.

This brief presents an analysis of the performance of kernel smoothing models used to estimate an unknown target function, addressing the case where the choice of the training set is part of the learning process. In particular, we consider a choice of the points at which the function is observed based on low-discrepancy sequences, which is a family of sampling methods commonly employed for efficient numerical integration. We prove that, under suitable regularity assumptions, consistency of the empirical risk minimization is guaranteed with a good rate of convergence of the estimation error, as well as the convergence of the approximation error. Simulation results confirm, in practice, the good theoretical properties given by the combination of kernel smoothing models with low-discrepancy sampling.

This issue marks the first anniversary issue of IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS after it changed its name from IEEE TRANSACTIONS ON NEURAL NETWORKS. I am happy to report that we had a great year! The number of new submissions in a year exceeded 1,000 for the first time in the history of TNN/TNNLS. IEEE TNN had a very successful development for 22 years from 1990 to 2011, and we have good reasons to believe that IEEE TNNLS will have many more years of successful growth.

The increase in efficiency of container terminals is addressed via an approach based on the optimisation of logistics operations. Toward this end, a discrete-time dynamic model of the various flows of containers that are inter-modally routed from arriving carriers to carriers ready for departure is proposed. On the basis of such a model, the decisions on the allocation of the available handling resources inside a container terminal are made according to the predictive-control approach by minimising a performance cost function over a forward horizon from the current time instant. Since both the dynamic equations and the cost function are in general nonlinear and since binary variables are used to model the departure or stay of a carrier, such decisions result from the on-line solution of a mixed-integer nonlinear programming problem at each time step. To solve this problem, two techniques are proposed that have to deal explicitly with the binary variables and with the nonlinearities of the model and the cost function. The first relies on the application of a standard branch-and-bound algorithm. The second is based on the idea of treating the decisions associated with the binary variables as step functions. Simulation results are reported to illustrate the pros and cons of such methodologies in a case study.

Approximate solution of a general T-stage stochastic optimal control problem is considered. It is known that discretizing uniformly the state components may lead dynamic programming to incur the "curse of dimensionality". Approximating networks, i.e., linear combinations of parametrized basis functions provided with density properties in suitable normed spaces, are then defined and used in two approximate methods (examples of such networks are one hidden layered feedforward neural networks, Radial Basis Functions, etc.). The first one consists in approximating the optimal cost-to-go functions in dynamic programming. The second method reduces the original functional optimization problem to a nonlinear programming one that is solved by means of stochastic approximation. Approximating networks of suitable types benefit by the property that, for the approximation of members of some classes of smooth functions, the number of parameters to be optimized and the number of samples to be used increase moderately with the dimensions of the arguments of the functions. The two methods are tested and compared in a test problem involving a 10-dimension state vector.