The growing demographic of older adults worldwide has intensified the demand for innovative cognitive and social stimulation solutions. While socially assistive robots integrated with serious games show promise, traditional robotic platforms often lack the flexibility to adapt dynamically to users’ fluctuating cognitive abilities and engagement levels. We propose a novel self-adaptive framework that combines the MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) control loop with the CORTEX cognitive robotics architecture. Implemented on the EBOv2 robotic platform, our system supports two popular serious games—"Simon Says" for attention and memory training, and "Pasapalabras" (a word quiz game) for semantic and linguistic stimulation. Specialized software agents within CORTEX dynamically modify these games’ structural and parametric elements, guided by multimodal inputs such as user performance metrics and emotional cues. A simulation-based evaluation using profiles derived from established cognitive models (ranging from high cognitive capacity to mild impairment) demonstrates the system’s adaptability and reliability. Our measurements show latency well below the 200 ms threshold—ensuring seamless real-time reconfiguration. Beyond response times, the adaptive difficulty mechanism effectively tailors challenge levels to each user profile, stabilizing within five sessions and improving overall engagement metrics compared to non-adaptive configurations. Users in the simulated high-capacity profile consistently progressed to advanced difficulty levels without frustration. In contrast, users with milder impairments benefited from gentle ramps in a challenge that enhanced task completion rates. These findings underscore the technical feasibility and potential therapeutic value of agent-based robotic systems for personalized cognitive stimulation. Future work will focus on long-term in situ evaluations with older adults in clinical and residential care settings, examining sustained improvements in cognitive resilience and psychosocial well-being.

The growing interest in connected autonomous vehicles (CAVs) has intensified the focus on technologies and algorithms that enhance behavior, comfort, and safety. Among these, the concept of Digital Twins (DT) represents an emerging field of research that is now beginning to be applied to autonomous systems. Traditional Advanced Driver-Assistance Systems (ADAS) can prevent real-time collisions using sensor data. However, we propose that employing a DT can enable the accounting for complex, simulated decisions before they occur in reality. This paper introduces an initial model of a Digital Twin, founded on an internal simulator aligned with vehicle control architecture, for real-time hazard prediction and effective decision-making. Our DT synchronizes with the vehicle’s state to simulate various hazardous scenarios in advance, allowing for preemptive actions. To support our hypothesis, we introduce an algorithm for the early detection of potential collisions between CAVs and pedestrians through the unsupervised simulation of diverse traffic scenarios. This solution integrates the CORTEX cognitive architecture with CARLA for internal simulation, leveraging probabilistic models to select optimal scenarios. Employing data from external pedestrian cameras, a particle filter predicts the most probable pedestrian trajectories via DT simulations, thereby informing safe maneuvers. Although the algorithm itself is established, the novelty of our approach lies in incorporating a simulator within the digital twin. This simulator, informed by real-time data on the vehicle’s and environment’s state, facilitates appropriate responses to unpredictable behaviors. We have conducted extensive tests with an actual autonomous electric vehicle on a university campus to validate the system’s predictive and adaptive functions.

The population of older adults is growing worldwide, and with it, there is a need for appropriate and effective cognitive therapies. Social robots have shown potential as therapeutic tools for older adults, providing companionship, entertainment, and therapeutic support. In particular, storytelling activities promote cognitive stimulation, socio-emotional skills, and simple entertainment. However, conventional storytelling methodologies concerning engagement, personalization, and interactivity exhibit a potential for greater diversity. These traditional approaches often require extensive preparation time, which can limit their feasibility and adaptability in diverse settings. Social robots can help overcome these limitations by providing a more interactive and engaging storytelling experience. In this paper, we present an approach to enhance the storytelling capabilities of the EBO robot, a social robot designed for interaction with older adult people. As a novelty, in addition to integrating the proposed system in the CORTEX cognitive architecture, we offer to include the therapist in the loop and integrate Artificial Intelligence techniques, including a Large Language Model, ChatGPT, to enable the robot to generate natural-sounding and engaging narratives, adjust its interactions based on the cognitive impairments, and align with the interests and preferences of the user. We demonstrate the effectiveness of our approach through a case study involving older adult participants and therapists. Our preliminary results show that the EBO robot, integrating AI techniques, can provide an interactive, customizable, and socially aware storytelling experience that promotes cognitive stimulation, socio-emotional skills, and simple entertainment that is engaging and enjoyable for older adults. Our approach could contribute to developing effective and engaging therapies for older adults using social robots as therapeutic tools.

It is widely accepted that in the future, robots will cooperate with humans in everyday tasks. Robots interacting with humans will require social awareness when performing their tasks which will require navigation. While navigating, robots should aim to avoid distressing people in order to maximize their chance of social acceptance. For instance, avoiding getting too close to people or disrupting interactions. Most research approaches these problems by planning socially accepted paths, however, in everyday situations, there are many examples where a simple path planner cannot solve all of the predicted robots’ navigation problems. For instance, requesting permission to interrupt a conversation if an alternative path cannot be determined requires deliberative skills. This article presents the Social Navigation framework for Autonomous robots in Populated Environments (SNAPE), where different software agents are integrated within a robotics cognitive architecture. SNAPE addresses action planning aimed at social-awareness navigation in realistic situations: it plans socially accepted paths and conversations to negotiate its trajectory to reach targets. In this article, the framework is evaluated in different use-cases where the robot, during its navigation, has to interact with different people in order to reach its goal. The results show that participants report that the robot’s behavior was realistic and human-like.

The use of socially assistive robots in real environments, such as nursing homes or geriatric residences, is spreading in recent years. Social robot navigation in these complex environments, with multiple users and dynamic objects, is an essential task for the next generation of service robots. This navigation must respect social rules, for example not to interrupt an interaction between two people or between a person and an object. Current navigation frameworks in robotics literature do not take into account the social complexity of the scenarios like, for example, the relation of the objects and their use by people with time. This article presents an approach to the idea of time-dependent social mapping. The main novelty is the definition and development of time-dependent affordance spaces. Each object has a zone that vary in function of time and the activities scheduled by the center's staff. Therefore, the planning of the best path by the robot takes into consideration this variation on time achieving a higher degree of social navigation. Several use cases have been performed in a simulated scenario to asses the robustness and validity of the proposal using these temporal variables.

This Research Full paper presents a systematic analysis. The objective of this work is to identify the educational context in which u-Learning was applied, as well as to evaluate which technologies are used to analyze the learning process in the period from 2012 to 2019. The quantitative analysis of the publications verified 5347 productions associated to the areas of sustainability, homecare, health, education, among others. The qualitative study of the data revealed: (i) the definition of u-Learning processes, referring to the individual behavioral repertoire, modified by the acquisition of certain knowledge; (ii) evaluation of the learning related to concrete situations in which the individual can manifest the knowledge in the acquired behaviors; and as for the application (iii) it was verified the inexistence of models for analysis of these process of change of behavior. Thus, from our research it was possible to observe, as a priority, the use of technologies as an instrument for the learning process, not being used in the measurement of knowledge. The findings point to the purpose of future studies that may indicate possible behavioral models that allow analyzing the behavior change and consequently the ubiquitous learning process.

Social navigation is a topic with enormous interest in autonomous robotics. Robots are gradually being used in human environments, working individually or collaborating with humans in their daily tasks. Robots in these scenarios have to be able to behave in a socially acceptable way and, for this reason, the way in which robots move has to adapt to humans and context. Proxemics has been extensively studied with the aim of improving social navigation. However, these works do not take into account that, in several situations, the personal space of the humans depends on the context (e.g., this human space is not the same in a narrow corridor than in a wide room). This work proposes the definition of an adaptive and flexible space density function that allows, on the one hand, to describe the comfort space of individuals during an interaction and, on the other hand, dynamically adapt its value in terms of the space that surrounds this interaction. In order to validate the performance, this article describes a set of simulated experiments where the robustness and improvements of the approach are tested in different environments.

Considering the widespread use of mobile robots in different parts of society, it is important to provide them with the capability to behave in a socially acceptable manner. Therefore, a research topic of great importance recently has been the study of Human-Robot Interaction. Autonomous navigation is a fundamental task in Robotics, and several different strategies that produce paths that are either length or time optimized can be found in the literature. However, considering the recent use of mobile robots in a more social context, the use of such classical techniques is restricted. Therefore, in this article we present a social navigation approach considering environments with groups of people. The proposal uses a density function to efficiently represent groups of people, and modify the navigation architecture in order to include the social behaviour of the robot during its motion. This architecture is based on the combined use of the Probabilistic Road Mapping (PRM) and the Rapidly-exploring Random Tree (RRT) path planners and an adaptation of the elastic band algorithm. Experimental evaluation was carried out in different simulated environments, providing insight on the performance of the proposed technique, which surpasses classical techniques with no proxemics awareness in terms of social impact.

Nowadays, the advance of the technology allows robots to acquire dense point clouds decreasing the price and increasing the performance. However, it is a hard task to deal with due to the large amount of points, the redundancy and the noise. This paper proposes an adaptable system to build a 3D feature model of point clouds using Gaussian Mixture Models. These 3D models are used in order to detect changes in the autonomous robot's working environment. The presented work describes an efficient change detection system based on two consecutive stages. First, a top-down approach estimates features using Gaussian Mixture Models. The presented new approach improves the performance of previous related works in terms of computational load and robustness, nevertheless the system is selection criteria dependent. Thus, the efficiency of different selection criteria are evaluated and compared in this paper. Experimental results demonstrate that the Minimum Distance Length (MDL) criteria outperforms the other studied methods. In the second stage, a change detection method is performed using the previously estimate Mixture of Gaussians. The proposed full system is able to detect changes using Gaussian Mixture Models with a reduced computational cost in relation to state-of-art algorithms.

Facial expressions are a rich source of communicative information about human behavior and emotion. This paper presents a real-time system for recognition and imitation of facial expressions in the context of affective Human Robot Interaction. The proposed method achieves a fast and robust facial feature extraction based on consecutively applying filters to the gradient image. An efficient Gabor filter is used, along with a set of morphological and convolutional filters to reduce the noise and the light dependence of the image acquired by the robot. Then, a set of invariant edge-based features are extracted and used as input to a Dynamic Bayesian Network classifier in order to estimate a human emotion. The output of this classifier updates a geometric robotic head model, which is used as a bridge between the human expressiveness and the robotic head. Experimental results demonstrate the accuracy and robustness of the proposed approach compared to similar systems.

There are several applications for which it is important to both detect and communicate changes in data models. For instance, in some mobile robotics applications (e.g. surveillance) a robot needs to detect significant changes in the environment (e.g. a layout change) which it may achieve by comparing current data provided by its sensors with previously acquired data (e.g. map) of the environment. This often constitutes an extremely challenging task due to the large amounts of data that must be compared in real-time. This paper proposes a framework to detect, and represent changes through a compact model. The main steps of the procedure are: multi-scale sampling to reduce the computation burden; change detection based on Gaussian mixture models; fitting superquadrics to detected changes; and refinement and optimization using the split and merge paradigm. Experimental results in various real and simulated scenarios demonstrate the approach's feasibility and robustness with large datasets.

The ability to detect perceptions which were never experienced before, i.e. novelty detection, is an important component of autonomous robots working in real environments. It is achieved by comparing current data provided by its sensors with a previously known map of the environment. This often constitutes an extremely challenging task due to the large amounts of data that must be compared in real-time. With respect to previously proposed approaches, this paper detects changes in 3D environment based on probabilistic models, the Gaussian Mixture Model, and a fast and robust combined constraint matching algorithm. The matching allows to represent the scene view as a graph which emerges from the comparison between Mixtures of Gaussians. Finding the largest set of mutually consistent matches is equivalent to find the maximum clique on a graph. The proposed approach has been tested for mobile robotics purposes in real environments and compared to other matching algorithms. Experimental results demonstrate the performance of the proposal.

Hybrid maps combine metric and topological information for efficiently managing large-scale environments. In a feature-based mapping framework, this paper describes the application of a spectral clustering approach for automatically detecting the transitions between subsequently traversed local maps. Contrary to recently proposed approaches, this algorithm considers each individual map feature as a node of a graph whose edges link two nodes if they are simultaneously observed. Thus, given a sequence of observations, an auxiliary graph is incrementally built whose edges carry non-negative weights according to the locality of the features. Given a feature, its locality defines the set of features that has been observed simultaneously with it at least once. At each execution of the mapping approach, the feature-based graph is split into two subgraphs using a normalized spectral clustering algorithm. If the graph partition is validated, the algorithm determines that the robot is moving into a new area and a new local map is generated. We have tested the proposed approach in real environments where features are obtained using 2D laser sensors or vision. Experimental results demonstrate the performance of the proposal.

This paper describes an efficient method for retrieving the 3-dimensional shape associated to novelties in the environment of an autonomous robot, which is equipped with a laser range finder. First, changes are detected over the point clouds using a combination of theGaussian mixture model(GMM) and theearth mover's distance(EMD) algorithms. Next, the shape retrieval is achieved using two different algorithms. First, new samplings are generated from each Gaussian function, followed by arandom sampling consensus(RANSAC) algorithm to retrieve geometric primitives. Furthermore, a new algorithm is developed to directly retrieve the shape according to the mathematical space of Gaussian mixture. In this paper, the set of geometric primitives has been limited to the setC = {sphere, cylinder, plane}. The two shape retrieval methods are compared in terms of computational cost and accuracy. Experimental results in various real and simulated scenarios demonstrate the feasibility of the approach.

In this paper, we describe a new approach which uses scale-invariant image features to estimate the motion of a stereo head. These point features are matched between pairs of frames and linked into image trajectories at video rate, generating what it is called visual odometry, i.e. motion estimates from visual input alone. With respect to previously proposed approaches, the main novelty of our proposal is that the matching between sets of features associated to stereo pairs and between sets of image features associated to consecutive frames are conducted by means of a fast combined constraint matching algorithm. Besides, the efficiency of the approach is increased by using a closed-form solution to estimate the final robot displacement between consecutive acquired frames. We have tested the proposed approach for navigational purposes in a real environment. Experimental results demonstrate the performance of the proposal.

In this paper, an approach for fitting a circle to 2-D data which represent only a small part of the curve is described. This approach deals with the particular case where data is specified in terms of its Cartesian coordinates and the errors in both coordinates are not independent. Besides, an associated uncertainty ellipse which describes the measurement error and the variance matrix associated to the estimated parameters are obtained. This method is particularly well designed to fit a circle to a set of measured range readings provided by a 2-D laser range finder when these range readings are specified in terms of its Cartesian coordinates. Therefore, it has been successfully applied to acquire circle-shaped landmarks in a mobile robotics navigation task.

This paper presents a geometrical feature detection system to use with conventional 2D laser rangefinders. This system consists of three main modules: data acquisition and pre-processing, rupture and breakpoint detection and feature extraction. The novelty of this system is a new efficient approach for natural feature extraction based on curvature estimation. This approach permits to extract and characterise line segments, corners and curve segments from the laser scan. Experimental results show that the proposed approach is very fast and permit to verify its effectiveness in indoor and outdoor environments