Defesas de Doutoramento INESC TEC
PhD Candidate full name:
José Carlos Miranda Nova Arnaud
Dissertation Title: Relationship between Digital Transformation and Digital Literacy of Local Public Administration employees – An Explanatory Model
Date: 2026-03-05 15:30
Location: UTAD | Escola de Ciências e Tecnologia | Auditório B0.01
Higher Education Institution: UTAD - Universidade de Trás-os-Montes and Alto Douro
Doctoral Programme: Doctoral Program Web Science and Technology
Research Centre: HumanISE
Principal Supervisor at INESC TEC: Henrique São Mamede
PhD Candidate full name:
Bruno Georgevich Ferreira
Higher Education Institution: University of Porto
Doctoral Programme: Doctoral Program in Informatic Engineering (FEUP)
Dissertation Title: Modular and Multi-Stage Semantic Perception System for Robotics
PhD dissertation defense day and hour: 27/02/2026 14:00
PhD dissertation defense location: Porto (UP) | Engineering (FEUP) | Administration Building | Room 104 - Sala de Atos
Public Announcment link: https://sigarra.up.pt/feup/pt/instal_geral.espaco_view?pv_id=73053
Abstract or Public Summary: Modular and Multi-Stage Semantic Perception System for Robotics The evolution of autonomous robotics benefits largely from the capacity to construct rich, naviga- ble, and semantic representations of the environment, even more so if shared with humans. While the advent of open-vocabulary scene graphs powered by Vision-Language Models (VLMs) has revolutionized perception, these systems face critical hurdles: high rates of hallucinations (False Positives), a lack of topological spatial context, and operational fragility due to heavy reliance on cloud connectivity. This thesis proposes the Hybrid Inference Perception and Mapping Sys- tem (HIPaMS), framework adaptable to a target system, likely a robotic system that interacts with humans. The HIPaMS is a modular framework designed to bridge the gap between low-level per- ception and high-level agentic reasoning. A Proof of Concept (PoC) was designed to implement the HIPaMS. This PoC enhances the state-of-the-art ConceptGraphs semantic mapping process and introduces a refined interaction system through four main contributions. First, it introduces the Hybrid Adaptable Resource-Aware Inference Mechanism (HARAIM), which dynamically or- chestrates internal models and settings based on runtime resource availability and optimization policies. This mechanism allows any optimization policy to adapt robotic system’s operation, pos- sibly allowing zero downtime during network failures, graceful degradation and/or operational ef- ficiency. Second, the semantic mapping pipeline is enhanced with rigorous False Positive filtering protocols, persona-based prompt engineering, and a broad collection of semantic information in an optimized manner during mapping. Third, a Room Semantic Segmentation Routine is proposed to provide topological information to the semantic map during interaction. This transforms un- structured, noisy detections into a hierarchically organized scene graph, anchoring objects within functional topological regions. Fourth, the robotic system now incorporates dynamic knowledge base via the Human-in-the-Loop (HITL) Agentic Retrieval-Augmented Generation (RAG)-based Interaction System (HARBIS). This interface uses short- and long-term memory to understand complex natural language queries. It enables the robot to learn continuously from user interac- tions, address gaps in perception and knowledge, maintain temporal consistency, and acknowledge its limitations by proactively asking for clarification. Extensive validation was conducted across 30 diverse environments, involving a total of 3300 interactive requests (depend on semantic map quality). The tested PoC processed 110 user requests per environment, categorized into: direct (30), indirect (30), graceful failure (30), follow-up (10) and time consistency (10). An ablation study was also performed to identify the impact of specific framework and PoC components. The results show that the PoC reduces False Positive detections by ≈ 86%, elevating mapping precision from a baseline of ≈ 0.28 to ≈ 0.68. Although strict filtering reduces raw recall, the integration of HITL learning increased the success rate for complex query resolution to ≈ 0.81, compared to baseline values of ≈ 0.48 and ≈ 0.55. Furthermore, the HIPaMS PoC reduced cloud inference costs by up to ≈ 84% in mapping and over ≈ 95% in interaction tasks while ensuring system sta- bility. The presented framework paves the way for increased robotic autonomy and efficiency. The presented PoC demonstrates superior performance, particularly for human-centered scenarios.
Research Centre: CRIIS
Principal Supervisor at INESC TEC: Armando Jorge Miranda de Sousa
Scientific Domain: [Artificial Intelligence] + [Robotics]
Keywords or Short sentences: Semantic Mapping, Open-Vocabulary Perception, Hybrid Inference Architecture, Adaptable Framework, Human-in-the-Loop, Retrieval-Augmented Generation (RAG), Topolog- ical Segmentation, Robot@VirtualHome, Vision-Language Models, Agentic AI, Operational Robustness.
PhD Candidate full name:
Hugo Miguel Oliveira de Sousa
Dissertation Title: Unfolding the Temporal Structure of Narratives
Date: 2026-02-25 14:30
Location: UP | FCUP | FC5 278
Higher Education Institution: University of Porto
Doctoral Programme: Doctoral Program in Computer Science (FCUP)
Research Centre: LIAAD
Principal Supervisor at INESC TEC: Alípio Jorge
Additional Supervisor: Ricardo Campos (UBI)
Scientific Domain: [Artificial Intelligence]
PhD Candidate full name:
Artur José Vilares Cordeiro
Higher Education Institution: UTAD - Universidade de Trás-os-Montes and Alto Douro
Doctoral Programme: Doctoral Program in Electrical and Computer Engineering
Dissertation Title: Configurable Perception Pipeline for Bin-picking in Industrial Scenarios
PhD dissertation defense: select day and hour: 2026-02-12 10:30:00
PhD dissertation defense location: UTAD - Universidade de Trás-os-Montes and Alto Douro | School of Sciences and Technology | Library | Room B- 1.04
Abstract or Public Summary: In today’s industry environment, picking systems are crucial in transforming internal logistic operations by enhancing automation, efficiency, and accuracy. Despite significant advancements, current perception approaches for picking system often fall short in dynamic and uncontrolled environments due to their reliance on static assumptions, leading to decreased performance. This thesis develops hybrid perception systems for picking operations by integrating artificial intelligence with traditional methodologies, aiming to create configurable systems adaptable to diverse dynamic environments. A novel data generation technique and a modular perception system were developed to address complex bin-picking challenges in both controlled and uncontrolled settings, incorporating new objects using only their 3D models and enabling the use of varied techniques for dynamic problem-solving. The system achieved a 91.81% average picking success rate in dynamic, cluttered, and unstructured environments, while also generating synthetic labeled data 440 times faster than manual real-data collection with comparable quality. This work provides a robust, modular perception and data generation solution, validated across industrial environments, offering a versatile tool for addressing challenging bin-picking tasks in diverse scenarios.
Research Centre: CRIIS
Principal Supervisor at INESC TEC: João Pedro Carvalho de Souza
Scientific Domain: [Computer Science and Engineering] + [Robotics]
Keywords or Short sentences: Perception systems; Robotic picking; Computer Vision; Machine learning