We introduce a novel approach for scalable domain adaptation in cloud robotics scenarios where robots rely on third–party AI inference services powered by large pre– trained deep neural networks. Our method is based on a downstream proposal–refinement stage running locally on the robots, exploiting a new lightweight DNN architecture, R2SNet. This architecture aims to mitigate performance degradation from domain shifts by adapting the object detection process to the target environment, focusing on relabeling, rescoring, and suppression of bounding–box proposals. Our method allows for local execution on robots, addressing the scalability challenges of domain adaptation without incurring significant computational costs. Real–world results on mobile service robots performing door detection show the effectiveness of the proposed method in achieving scalable domain adaptation.

Multi-robot rendezvous and exploration are fundamental challenges in the domain of mobile robotic systems. This paper addresses multi-robot rendezvous within an initially unknown environment where communication is only possible after the rendezvous. Traditionally, exploration has been focused on rapidly mapping the environment, often leading to suboptimal rendezvous performance in later stages. We adapt a standard frontier-based exploration technique to integrate exploration and rendezvous into a unified strategy, with a mechanism that allows robots to re-visit previously explored regions thus enhancing rendezvous opportunities. We validate our approach in 3D realistic simulations using ROS, showcasing its effectiveness in achieving faster rendezvous times compared to exploration strategies.

The Rey Osterrieth complex figure (ROCF) is one of the most used neuropsychological tests for the assessment of mild cognitive impairment (MCI) and dementia. In the copy test, the patient has to draw a replica of a 18-pattern image and the outcome is a score based on the accuracy of the overall drawing. The standard scoring system however have limitations related to its subjective nature and its inability to evaluate other cognitive domains than constructional abilities. Previous works addressed those problems by proposing tablet-based automated evaluation systems. Even promising, such methods are still far away from clinical validation and translation. In this work, we developed a decision support system (DSS) for the evaluation of the ROCF copy test in the common practice using retrospective information from previously performed drawings. The goal of our system was to support the professionals providing a qualitative judgement for each of the 18 patterns, estimating the most probable diagnosis for the patient, and identifying the main signs associated to the obtained diagnosis. A total of 250 human evaluated ROCF copies were scanned from 57 healthy subjects, 131 individuals with MCI, and 62 individuals with dementia. The images were pre-processed and analysed using both computer vision and deep learning techniques to assign a qualitative label to the 18 patterns. Then, the 18 labels were used as features in 3 binary (healthy VS MCI, healthy VS dementia, MCI VS dementia) and a 3-class classifications with model explanation (SHAP). Very good to excellent performance were obtained in all the diagnosis classification tasks. Indeed, an accuracy of about 85%, 91%, and 83% was obtained in discriminating healthy subjects from MCI, healthy subjects from dementia and MCI from dementia respectively. An accuracy of 73% was achieved in the 3-class classification. The model explanation showed which patterns are responsible for each prediction and how the importance of some patterns changes according to the severity of the cognitive decline. The proposed DSS enriches the standard evaluation and interpretation of the ROCF copy test. Being trained with retrospective knowledge, the performance of the DSS can be further enhanced by extending the dataset with existing ROCF copies.

State of the art mapping algorithms can produce high-quality maps. However, they are still vulnerable to clutter and outliers which can affect map quality and in consequence hinder the performance of a robot, and further map processing for semantic understanding of the environment. This paper presents ROSE, a method for building-level structure detection in robotic maps. ROSE exploits the fact that indoor environments usually contain walls and straight-line elements along a limited set of orientations. Therefore metric maps often have a set of dominant directions. ROSE extracts these directions and uses this information to segment the map into structure and clutter through filtering the map in the frequency domain (an approach substantially underutilised in the mapping applications). Removing the clutter in this way makes wall detection (e.g. using the Hough transform) more robust. Our experiments demonstrate that (1) the application of ROSE for decluttering can substantially improve structural feature retrieval (e.g., walls) in cluttered environments, (2) ROSE can successfully distinguish between clutter and structure in the map even with substantial amount of noise and (3) ROSE can numerically assess the amount of structure in the map.

In several applications, autonomous mobile robots benefit from knowing the structure of the indoor environments where they operate. This knowledge can be extracted from the metric maps built (e.g., using SLAM algorithms) from the data perceived by the robots' sensors. The layout is a way to represent the structure of an indoor environment with geometrical primitives. Most of the current methods for reconstructing the layout from a metric map represent the parts of the environment that have been fully observed. In this paper, we propose an approach that predicts the layout of rooms which are only partially known in a 2D metric grid map. The prediction is made according to the global structure of the environment, as identified from its known parts. Experiments show that our approach is able to effectively predict the layout of several indoor environments that have been observed to different degrees.

The employment of Social Assistive Robots (SARs) for monitoring elderly users represents a valuable gateway for at-home assistance. Their deployment in the house of the users can provide effective opportunities for early detection of Mild Cognitive Impairment (MCI), a condition of increasing impact in our aging society, by means of digitalized cognitive tests. In this work, we present a system where a specific set of cognitive tests is selected, digitalized, and integrated with a robotic assistant, whose task is the guidance and supervision of the users during the completion of such tests. The system is then evaluated by means of an experimental study involving potential future users, in order to assess its acceptability and identify key directions for technical improvements.

The ageing of the population deeply impacts on the social costs relative to health care. The use of modern technologies is one of the most promising approaches, under current study, to reduce such impact. In this demonstration, we propose a framework that can be employed for at-home assessment of Mild Cognitive Impairment (MCI). It is composed by a set of digitalized cognitive tests, developed from their paper-and-pencil counterparts, and by a Virtual Caregiver, which oversees the test execution and provides instructions.

The development of good experimental methodologies for robotics takes often inspiration from general principles of experimental practice. Repeatability prescribes that experiments should involve several trials in order to guarantee that results are not achieved by chance, but are systematic, and statistically significant trends can be identified. In this paper, we propose an approach to improve the repeatability of experiments performed in robotics. In particular, we focus on the domain of SLAM (Simultaneous Localization And Mapping) and we introduce a system that exploits simulations to generate a large number of test data on which SLAM algorithms are automatically evaluated in order to obtain consistent results, according to the principle of repeatability.

Semantic mapping for autonomous mobile robots includes the place classification task that associates semantic labels (like `corridor' or `office') to rooms perceived in indoor environments. The mainstream approaches to place classification are characterized by local reasoning, where only features relative to the neighbourhood of each room are considered. In this paper, we propose a method for global reasoning on the whole structure of buildings, considered as single structured objects. We use a statistical relational learning algorithm, called kLog, and we compare it against a classifier, Extra-Trees, which resembles classical local approaches, in three tasks: classification of rooms, classification of entire floors of buildings, and validation of simulated worlds. Our results show that our global approach performs better than local approaches when the classification task involves reasoning on the regularities of buildings and when available information about rooms is coarse-grained.

Consider a mobile robot exploring an initially unknown school building and assume that it has already discovered some classrooms, offices, and bathrooms. What can the robot infer about the presence and the locations of other classrooms and offices in the school building? This paper makes a step toward providing an answer to the above question by proposing a system based on a generative model that is able to represent the topological structures and the semantic labeling schemas of buildings and to predict the structure and the schema for unexplored portions of these environments. We represent the buildings as undirected graphs, whose nodes are rooms and edges are physical connections between them. Given an initial knowledge base of graphs, our approach, relying on a spectral analysis of these graphs, segments each graph for finding significant subgraphs and clusters them according to their similarity. A graph representing a new building or an unvisited part of a building is eventually generated by sampling subgraphs from clusters and connecting them.