Abstract
This work seeks to design and implement a humanoid robotic kick for situations where the robot is moving for the RoboCup simulation 3D robotic soccer league. It employs Reinforcement Learning (RL) techniques, namely the Proximal Policy Optimization (PPO) algorithm to create fast and reliable skills. The kick was divided into 6 cases according to initial conditions and separately trained for each of the cases. A series of kicks, both static and in motion, using two different gaits were developed. The kicks obtained show very high reliability and, when compared to state of the art kicks, displayed a very high time performance improvement. This opens the door to more dynamic games with faster kicks in the RoboCup simulation 3D league.

Abstract
The media sector is constantly evolving and, in the last few years, such evolution has been driven by a number of convergence paradigms, notably, that between broadband and broadcast technologies with the introduction of IT and IP technology. The present trend is to switch totally from a closed niche that uses highly specialized equipment to off-the-shelf IT-centric solutions, offering easy configuration and remote operation. The aim is to enable common computers to be turned into highly capable media devices and act as connected objects adopting an IoT-like paradigm. This vision, though, is not implemented easily, given that most media industry professionals do not yet feel comfortable operating in the IT technology space and also due to the stringent requirements that exist in this industry. The Joint Task Force on Networked Media is defining specifications that aim at overcoming such existing barriers. In this article we present a novel solution that follows the guidelines delivered by this group to set up a remotely operated media production facility, totally based on IP and IT technology, constituting a step forward the realization of the IoT concept in professional media environments. The focus is on two complementary components, namely, the GUI Agent and the MW Agent, which are not covered by the defined specifications but that are crucial to speed up the deployment of concrete solutions that can be easily operated by non-IT and non-IP experts in a transparent and ubiquitous way. © 2020 TRON Forum.

Abstract
This paper investigates the learning of both low-level behaviors for humanoid robot controllers and of high-level coordination strategies for teams of robots engaged in simulated soccer. Regarding controllers, current approaches typically hand-tune behaviors or optimize them without realistic constraints, for example allowing parts of the robot to intersect with others. This level of optimization often leads to low-performance behaviors. Regarding strategies, most are hand-tuned with arbitrary parameters (like agents moving to pre-defined positions on the field such that eventually they can score a goal) and the thorough analysis of learned strategies is often disregarded. This paper demonstrates how it is possible to use a distributed framework to learn both low-level behaviors, like sprinting and getting up, and high-level strategies, like a kick-off scenario, outperforming previous approaches in the FCPortugal3D Simulated Soccer team.

Abstract

Abstract
The overtaking maneuver can be often dangerous and risky, especially when driving in rural roads, with a significant probability of leading to fatalities and serious injuries. Vehicle communication emerges as one of the leading technologies to improve the driving experience and road safety. This article presents a system designed to augment road safety in an Overtaking Maneuver, providing the ahead vehicle's Line-of-Sight by real-time video-streaming. It uses the C-V2X Transmission Mode 4 that, building upon cellular technology, has evolved as one of the main standards for vehicle communication. Results show an effective application of the system in real overtaking scenarios, enhancing the safety and proving its usefulness. Whilst having some impact on the latency, the solution incurred in low packet losses and provided good video quality.

Abstract
Mobile Augmented Reality (MAR) systems have emerged and greatly evolved in the last two decades. They have application in many domains, most notably in the field of Cultural Heritage (CH) and tourism, where people tend to rely on smartphones when visiting a new city to obtain additional information on the city landmarks. Expectations are that they obtain precise and tailored information to the visitor’s needs. Therefore, researchers started to investigate innovative approaches for presenting and suggesting digital content related to cultural and historical places. This article presents a novel MAR application, NearHeritage, which uses emergent technologies to assist visitors in finding and exploring Cultural Heritage. The research focuses on combining the use of context-awareness with Augmented Reality (AR). By sensing the context surrounding the user, the NearHeritage app discloses not only the list of nearby points-of-interest (POI) but also detailed information about the POIs in the form of AR content adapted to the user context. The solution presented uses built-in sensors of Android devices and takes advantage of various APIs (Foursquare API, Google Maps API and IntelContextSensing SDK) to retrieve information about the landmarks and the visitor context. Results from initial experimentation indicate that the concept of a context-aware MAR application can improve the user experience in discovering and learning more about Cultural Heritage, creating an interactive, enjoyable and unforgettable adventure. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.