Despite the pervasive use of deep neural networks (DNN) in numerous domains, their training and deployment mainly resides on computationally intensive pipelines. In turn, this restrains the use of DNN for autonomous robots due to insufficient computational power and the need to regulate power consumption for computation as well as actuation purposes. To address these limitations, this work presents the LEASARD project which aims to increase navigation autonomy while preserving energy autonomy of aerial drones for search-and-rescue applications, that need to leverage DNN for scene perception and drone control. In detail, we advocate the enhancement of sensing and processing tasks through low-energy hardware such as event cameras and Field-Programmable Gate Arrays (FPGAs), so as to increase the efficiency of obstacle-avoidance and robot vision algorithms in a way that capitalizes DNN architectures that are adapted to this new hardware. Overall, the contributions of the project span over algorithm, software and hardware design for improving on-board, drone processing capabilities in adverse scenarios.
Financial regulatory documents are characterized by their fine-grained complexity and pronounced heterogeneity, featuring specialized domain-specific content and diverse structural formats that vary across different regulatory frameworks and jurisdictions. These characteristics challenge modern Question-Answering (QA) systems, which often suffer from limited domain adaptation, poor interpretability, and hallucinatory problems. This work was conducted within a banking and software company, where such challenges directly impact regulatory compliance efforts. Our goal is to introduce RegulQA, a hybrid QA system that can extract accurate, logical, and comprehensible answers from unstructured regulatory documents. RegulQA integrates knowledge graph reasoning, semantic search, and retrieval-augmented generation using large language models. Experimental evaluation shows that RegulQA improves QA performance and significantly reduces hallucination rates. The baseline model employing only the LLM demonstrated a hallucination rate of 24%, whereas the proposed approach, combining knowledge graph reasoning and semantic retrieval with LLM reasoning, effectively reduced the hallucination rate to approximately the half. This integrated approach also yielded the best balanced overall scores across key qualitative attributes, including coverage, non-redundancy, readability, and response quality.
As autonomous robots increasingly operate in private spaces, software-based privacy protection remains opaque and vulnerable to reconstruction attacks. We propose HuLePA-RoV, a human-legible, privacy-aware robotic vision framework that enforces privacy at the physical sensor layer. By default, a mechanically actuated optical element limits perception to low-frequency visual information, enabling navigation and coarse object classification while remaining physically incapable of capturing identifiable facial details. High-resolution sensing is enabled only through authenticated events and visible mechanical reconfiguration, providing users with an immediate and verifiable indication of the robot's privacy state. This approach shifts privacy from invisible software enforcement to observable hardware control, fostering trust and transparency in human-robot interaction.
