Artificial intelligence and robotics are advancing at an incredible pace; however, there is a risk associated with the data privacy and personal information of users interacting with these systems and platforms. In this context, the federated learning approach emerged to enable large-scale, distributed learning without the need to transmit or store any information necessary to train the learning models. In a previous paper, we presented a system capable of detecting, locating, and classifying what kind of contact occurs between humans and one of our robots using innovative contact microphone technology. In this work we go further, improving the previously presented touch system with a multi-user, multi-robot, distributed, and scalable learning approach that is able to learn in a collaborative and incremental way while respecting the privacy of the user’s information. The system has been successfully evaluated in a real environment with 28 different users divided in 7 different groups. To assess the performance of our system with this federated learning approach, we compared it to the same distributed learning system without federated learning. That is, the control group for this comparison is a central node directly receiving all the training examples obtained by each robot locally. We found that in this context the inclusion of federated learning improves the results concerning traditional distributed learning.

The external aspects of a robot affect how people behave and perceive it while interacting. In this paper, we study the importance of the mouth displayed by a social robot and explore how different designs of an artificial LED-based mouths alter the participants' judgments of a robot's attributes and their attention to the robot's message. We evaluated participants' judgments of a speaking robot under four conditions: 1) without a mouth; 2) with a static smile; 3) with a vibrating, wave-shaped mouth; and 4) with a moving, human-like mouth. A total of 79 participants evaluated their perceptions of an on-video robot showing one of the four conditions. The results show that the presence of a mouth, as well as its design, alters the perception of the robot. In particular, the presence of a mouth makes the robot to be perceived more lifelike and less sad. The human-like mouth was the one participants liked the most and, along with the smile, they were the friendliest ones. On the contrary, participants rated the mouthless robot and the one with the wave-like mouth as the most dangerous ones.

This LBR describes a novel method for user recognition in HRI, based on analyzing the peculiarities of users voices, and specially focused at being used in a robotic system. The method is inspired by acoustic fingerprinting techniques, and is made of two phases: a)enrollment in the system: the features of the user's voice are stored in files called voiceprints, b)searching phase: the features extracted in real time are compared with the voiceprints using a pattern matching method to obtain the most likely user (match).

Research on dialog systems is a very active area in social robotics. During the last two decades, these systems have evolved from those based only on speech recognition and synthesis to the current and modern systems, which include new components and multimodality. By multimodal dialogue we mean the interchange of information among several interlocutors, not just using their voice as the mean of transmission but also all the available channels such as gestures, facial expressions, touch, sounds, etc. These channels add information to the message to be transmitted in every dialogue turn. The dialogue manager (IDiM) is one of the components of the robotic dialog system (RDS) and is in charge of managing the dialogue flow during the conversational turns. In order to do that, it is necessary to coherently treat the inputs and outputs of information that flow by different communication channels: audio, vision, radio frequency, touch, etc. In our approach, this multichannel input of information is temporarily fused into communicative acts (CAs). Each CA groups the information that flows through the different input channels into the same pack, transmitting a unique message or global idea. Therefore, this temporary fusion of information allows the IDiM to abstract from the channels used during the interaction, focusing only on the message, not on the way it is transmitted. This article presents the whole RDS and the description of how the multimodal fusion of information is made as CAs. Finally, several scenarios where the multimodal dialogue is used are presented.

Human–robot interaction (HRI) 1 1HRI can be defined as the study of humans, robots, and the ways in which they influence each other. * Portions of this work were previously presented at the 26th Robotics Symposia which was held on-line in March 2021. is one of the main fields in the study and research of robotics. Within this field, dialogue systems and interaction by voice play an important role. When speaking about human–robot natural dialogue we assume that the robot has the capability to accurately recognize what the human wants to transmit verbally and even its semantic meaning, but this is not always achieved. In this article we describe the steps and requirements that we went through in order to endow the personal social robot Maggie, developed at the University Carlos III of Madrid, with the capability of understanding the natural language spoken by any human. We have analyzed the different possibilities offered by current software/hardware alternatives by testing them in real environments. We have obtained accurate data related to the speech recognition capabilities in different environments, using the most modern audio acquisition systems and analyzing not so typical parameters such as user age, gender, intonation, volume, and language. Finally, we propose a new model to classify recognition results as accepted or rejected, based on a second automatic speech recognition (ASR) opinion. This new approach takes into account the precalculated success rate in noise intervals for each recognition framework, decreasing the rate of false positives and false negatives.