Purpose of Review: To present the multi-faceted aspects of communication between robot and humans (HRI), putting in evidence that it is not limited to language-based interaction, but it includes all aspects that are relevant in communication among physical beings, exploiting all the available sensor channels.Recent Findings: For specific purposes, machine learning algorithms could be exploited when data sets and appropriate algorithms are available.Summary: Together with linguistic aspects, physical aspects play an important role in HRI and make the difference with respect to the more limited human-computer interaction (HCI). A review of the recent literature about the exploitation of different interaction channels is presented. The interpretation of signals and the production of appropriate communication actions require to consider psychological, sociological, and practical aspects, which may affect the performance. Communication is just one of the functionalities of an interactive robot and, as all the others, will need to be benchmarked to support the possibility for social robots to reach a real market.

Scientific experiments and robotic competitions share some common traits that can put the debate about developing better experimental methodologies and replicability of results in robotics research on more solid ground. In this context, the Robot Competitions Kick Innovation in Cognitive Systems and Robotics (RoCKIn) project aims to develop competitions that come close to scientific experiments, providing an objective performance evaluation of robot systems under controlled and replicable conditions. In this article, by further articulating replicability into reproducibility and repeatability and by considering some results from the 2014 first RoCKIn competition, we show that the RoCKIn approach offers tools that enable the replicability of experimental results.

We propose a framework to estimate player enjoyment preference from physiological signals. This can produce objective measures that could be used to adapt dynamically a game to maintain the player in an optimal status of enjoyment. We present a case study on The Open Racing Car Simulator (TORCS) video game. In particular, we focus both on the experimental protocol, which we designed with special attention to produce physiological responses related to the game experience only, and on signal analysis, which produces a simple and general model good enough to estimate player enjoyment preference in real applications.