Effective team strategies and joint decision-making processes are fundamental in modern robotic applications, where multiple units have to cooperate to achieve a common goal. The research community in Artificial Intelligence and Robotics has launched robotic competitions to promote research and validate new approaches, by providing robust benchmarks to evaluate all the components of a multi-agent system-ranging from hardware to high-level strategy learning. Among these competitions RoboCup has a prominent role, running one of the first worldwide multi-robot competition (in the late '90s), challenging researchers to develop robotic systems able to compete in the game of soccer. Robotic soccer teams are complex multi-robot systems, where each unit shows individual skills, and solid teamwork by exchanging information about their local perceptions and intentions. In this survey, we dive into the techniques developed within the RoboCup framework by analyzing and commenting on them in detail. We highlight significant trends in the research conducted in the field and to provide commentaries and insights, about challenges and achievements in generating decision-making processes for multi-robot adversarial scenarios. As an outcome, we provide an overview a body of work that lies at the intersection of three disciplines: Artificial Intelligence, Robotics and games.

Melanoma is the deadliest form of skin cancer. Early diagnosis of malignant lesions is crucial for reducing mortality. The use of deep learning techniques on dermoscopic images can help in keeping track of the change over time in the appearance of the lesion, which is an important factor for detecting malignant lesions. In this paper, we present a deep learning architecture called Attention Squeeze U-Net for skin lesion area segmentation specifically designed for embedded devices. The main goal is to increase the patient empowerment through the adoption of deep learning algorithms that can run locally on smartphones or low cost embedded devices. This can be the basis to (1) create a history of the lesion, (2) reduce patient visits to the hospital, and (3) protect the privacy of the users. Quantitative results on publicly available data demonstrate that it is possible to achieve good segmentation results even with a compact model.

Large Language Models (LLMs) and Visual Language Models (VLMs) are attracting increasing interest due to their improving performance and applications across various domains and tasks. However, LLMs and VLMs can produce erroneous results, especially when a deep understanding of the problem domain is required. For instance, when planning and perception are needed simultaneously, these models often struggle because of difficulties in merging multi-modal information. To address this issue, fine-tuned models are typically employed and trained on specialized data structures representing the environment. This approach has limited effectiveness, as it can overly complicate the context for processing. In this paper, we propose a multi-agent architecture for embodied task planning that operates without the need for specific data structures as input. Instead, it uses a single image of the environment, handling free-form domains by leveraging commonsense knowledge. We also introduce a novel, fully automatic evaluation procedure, PG2S, designed to better assess the quality of a plan. We validated our approach using the widely recognized ALFRED dataset, comparing PG2S to the existing KAS metric to further evaluate the quality of the generated plans.

The analysis and processing of satellite remote sensing data have established new paradigms to observe the Earth and to study climate changes. Data collected by sensors and systems operating across the entire frequency spectrum carry on enormous information content. They enable systematic retrieval and characterization of bio-geophysical variables. The retrieval of these variables, however, is often hindered by limitations such as missing data, low spatial and temporal resolutions, and insufficient co-located ancillary data. These challenges arise due to factors such as sensor limitations, dead pixels, cloud cover, and discontinuous data acquisition. To overcome these issues, numerous techniques have been proposed, ranging from classical interpolation methods and spectral transforms to modern artificial intelligence-based approaches. This paper presents a comprehensive and critical review of satellite remote-sensing data augmentation methods, focusing on how interpolation theory and artificial intelligence techniques can be effectively applied to reconstruct missing data and enhance the quality of retrieved bio-geophysical variables. This roadmap offers a comprehensive framework that categorizes and evaluates existing methods, highlights their strengths and limitations, and identifies promising areas for future exploration.