Preparing students for dealing with a world more and more densely populated with physical machines that possess learning capabilities, e.g. intelligent robots, is of the utmost importance in engineering. In this paper, we describe and analyse a design of interactive sessions devoted to the application of some machine learning (ML) methods within a master degree subject named “Cognitive Robotics”, in particular, reinforcement learning (RL), a technique that allows the machine to autonomously learn decision-making in a physical environment. The paper contains a complete depiction of the interactive teaching sessions, implemented with reasonable cost and resources thanks to a suitable mixture of the constructivist and instructivist paradigms. It also gathers the experiences of students and teachers through both qualitative and quantitatively indicators. GRAPHICAL ABSTRACT

This paper deals with the modeling of the delays in the transmission of sensory data coming from a networked telerobot, which would allow us to predict future times of arrival and provide guarantees on the time requirements of these systems. Considering these delay sequences as an uni-dimensional temporal signal, they easily exhibit rich stochastic behavior—abrupt changes of regime and bursts—due to the heterogeneity of the hardware and software components in the data path. There exist approaches for modeling this kind of signals without explicit knowledge of the system components: state-space reconstruction, hidden Markov models, neural networks, etc., but they are mostly focused on the stochasticity of the network only, without taking into account other elements in the sensory flow that also have an important influence in the delays. Previously, we have proposed simpler statistical methods that do not require any component knowledge either and are suitable for more lightweight implementations (e.g., in mobile phone interfaces). In this sense, we report elsewhere a log-normal three-parametrical model that fits reasonably well these delays as long as change detection is completely solved. Now we propose a more flexible solution: the log-logistic distribution, which has been found to fit delays better than the log-normal. In addition, we present two algorithms to model an entire delay signal, including abrupt nonlinearities, based on the log-logistic assumption. Our results show quite good fittings of real datasets gathered from a number of combinations of sensors, networks, and application software, provided that some mild assumptions hold.

Numerous time synchronization methods have been proposed during the last decades targeted at devices transmitting in general-topology networks, e.g. wireless sensor networks. Interestingly enough, there are still sensorics applications that, from the data flow point of view, just consist of pairs of devices –typically, being one of them a central controller shared among all pairs–; this is common in embedded systems, mobile robots, factory cells, domotic installations, etc. In these cases, time synchronization takes the form of the estimation of the relative drifts and offsets of these pairs of clocks, thus the quality, guarantees and computational cost of the methods become crucial. Under that specific perspective, it is still possible to propose novel approaches that are computationally efficient while providing guarantees on the resulting estimates for the relative clocks relation. In particular, solving the synchronization problem through a geometrical interpretation is specially suitable, since it provides both efficiency and sound estimates with hard bounds. In this paper we analyze a direct geometrical approach for pairwise systems that, through a more direct formulation of the common geometrical setting, improves efficiency and adapts better to diverse stochastic transmissions, while providing good estimates. We demonstrate its advantages with a real low-cost, embedded test bed that can be appropriately instrumented for measuring times, comparing its performance against previous synchronization approaches suitable for pairwise systems.