Lorenzo Valente

Problem: Traditional VR haptic interface (e.g. controllers) are rigid and static (not easy to switch from holding a controller and hands-free interaction), limiting the immersive experience and failing to provide dynamic haptic feedback.
Objective:
Develop a dynamic, shape-changing VR controller that can be intermittently available at user palm, with changing shape, enhancing the tactile experience and providing more immersive interactions.
Approach: Design and implement proof-of-concept for an inflatable haptic interface. These can be based on pneumatic pouches or on mechanical origami-based or fullerene structures. Shape shall be adjusted in real-time, profile modulated over main axis.
Expected Impact:
Enhanced presence and fidelity of interactions requiring switching between hands interaction and passive props.
Alternative Applications/Target use case variants: It is possible to tailor the target use case to diverse student interests. Rather than developing a hand-held controller, this methodology can be utilized for racing simulator accessories (car or plane) or a robotic flange.
References
https://arxiv.org/html/2501.18764v1
https://dl.acm.org/doi/pdf/10.1145/3472749.3474782
https://dl.acm.org/doi/10.1145/3242587.3242628

Problem:
Traditional Air Traffic Control (ATC) systems can lead to high cognitive workload, especially with increasing air traffic density and the introduction of new air vehicles like drones. Current visualization methods may not fully support complex trajectory planning and real-time monitoring.
Objective:
Augmented Reality (AR) tactical table for ATC that enhances situational awareness, supports efficient trajectory planning and monitoring, and includes Early Warning Systems (EWS).
Approach:
Develop an AR-enabled tactical table visualizing the urban airspace, including all aircraft (traditional and Urban Air Mobility), their trajectories, and potential conflicts. Integrate semi-automatic processes to assist controllers, reducing cognitive workload while maintaining human oversight.
Multiple theses are available under this project:
– AI/ML backend only: Design and develop the EWS system based on ML and Foundational Models (based on Radio Communications, ATC Visualization systems (radar, GPS), etc.
– XR front-end/dashboard: Design and develop the XR visualization
Expected Impact:
Improved decision-making speed and accuracy; reduced cognitive workload for air traffic controllers; enhanced safety through advanced EWS; efficient management of complex and dense urban airspaces.
References:
https://ieeexplore.ieee.org/document/10764466, https://ieeexplore.ieee.org/document/9089606, https://dl.acm.org/doi/10.1145/3385378.3385380

Several collaborations are currently in place with the Piedmont Region Civil Protection and Forest Firefighting Unit, which are actively involved in emergency management and first responder training. In this context, a number of serious games and interactive experiences leveraging Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) technologies have already been developed to support the education and training of firefighters, volunteers, students, and decision makers.
Multiple thesis opportunities are available and will be developed mainly at Politecnico di Torino in collaboration with the above-mentioned institutions.
The proposed activities build upon an existing MR-based training ecosystem previously developed through several research and thesis projects, including the “Augmented Sand Table for Forest Firefighting”, currently supporting wildfire simulation scenarios based on the Cell2Fire framework. Current research activities focus on extending this ecosystem toward more immersive, scalable, and interoperable XR training solutions for emergency-response scenarios, with particular emphasis on wildfire management and hydrogeological risk mitigation. The activities may involve the use of Unity, XR headsets, simulation middleware, networking solutions, and advanced interaction techniques.
The objective of the first thesis is to extend and enhance existing immersive XR training applications for first responders operating in complex emergency scenarios, including wildfire suppression activities involving both ground and aerial operations. The work may address the integration of more realistic environmental simulations, interactive tools, and collaborative functionalities aimed at improving training effectiveness and operational realism. Particular attention may be devoted to fire and smoke simulation, immersive interaction techniques, real-time performance optimization, and interoperability with external simulation systems.
The goal of the second thesis is to evolve the existing simulation ecosystem through the development of advanced authoring tools and workflow solutions aimed at simplifying and accelerating the generation, configuration, and customization of XR training scenarios and case studies. The work may address procedural and data-driven scenario generation techniques, terrain editing solutions, and interoperability mechanisms for integrating heterogeneous simulation frameworks into scalable and reusable XR training ecosystems. A particular focus may be placed on extending the current Cell2Fire-based simulation pipeline toward additional emergency-management scenarios, including the integration of simulation systems addressing hydrogeological risks such as floods and terrain evolution.
The third thesis focuses on the evolution of previously developed XR applications into a unified multi-layer and multi-user ecosystem for coordinated emergency management. The resulting platform should support supervisors and decision makers in monitoring, coordinating, and evaluating the actions of distributed teams operating within interconnected simulation scenarios. The work may involve the synchronization of heterogeneous simulators, the implementation of collaborative XR interaction paradigms, the development of monitoring and analytics solutions, and the exploration of AI-assisted approaches for scenario management and operational analysis. The thesis may also explore the integration of immersive simulation modules for aerial wildfire suppression operations involving helicopters and Canadair aircraft within larger coordinated simulation environments.
References:
– https://www.knowledge-share.eu/en/software/tavolo-di-sabbia-aumentato-per-la-lotta-agli-incendi-boschivi
– https://ieeexplore.ieee.org/document/10902197
– https://ieeexplore.ieee.org/abstract/document/8576892

Problem:
Navigating large-scale virtual environments remains an open challenge in VR research. World-in-Miniature (WIM) interfaces have been studied almost exclusively in flat environments, leaving critical issues unresolved regarding scale management, occlusion handling, and the topographic complexity of real-world spaces.
Objective:
Extend and deepen WIM research in complex VR contexts, addressing challenges related to: multi-story indoor environments, occlusion management, adaptive scaling techniques, alternative input modalities (mid-air gestures, eye-gaze control), and hierarchical navigation through Points of Interest (POIs).
Approach: Design and prototype novel WIM techniques and evaluate them through controlled user studies, measuring navigation efficiency, cognitive load, and spatial knowledge acquisition.
Expected Impact:
Effective WIM techniques for topographically complex VR environments.

Context and Motivation:
In the era of Industry 4.0 and the Metaverse, Digital Twins (DT) have become indispensable for real-time monitoring, predictive maintenance, and immersive training. However, the creation of high-fidelity Digital Twins remains a significant bottleneck. Traditionally, this is a manual, labor-intensive process—especially when the object’s functional behavior must be modeled and coded from scratch to match its physical counterpart.
Problem Statement:
Current Digital Twin generation methodologies focus primarily on static geometry. However, most industrial assets and consumer products are Cyber-Physical Systems (CPS) or articulated objects (e.g., hardware tools, machinery with levers, knobs, and buttons) characterized by specific functional behaviors. For Extended Reality (XR) simulations to be effective, a Digital Twin must exhibit “Physical-Behavioral Symmetry”: it must not only look like the physical object but also mimic its kinematic constraints and logic. Manually defining multi-part hierarchies, kinematic joints, and Finite State Machine (FSM) logic for every asset is time consuming and require specialized skills (3d modeling, programming).
Objectives :
The objective of this thesis is to propose an end-to-end pipeline that reconstructs articulated/cyber-physical 3D models from standard video sequences and automatically synthesizes the underlying code/logic required to drive their behavior in virtual environments (e.g. Unity application).
Innovation and Expected Impact:
Students may leverage existing technologies as a starting point—such as PartGen for part-based reconstruction and LLMs for code generation—to either extend these frameworks or propose a novel, integrated Machine Learning-based approach. The successful outcome of this thesis will be a framework capable of transforming a simple videos into a “Smart Asset”: a ready-to-use, interactive Digital Twin for XR that possesses both geometric fidelity and functionality.

Haptic feedback is a fundamental component of immersive Virtual Reality (VR) experiences, especially in scenarios that involve manual interactions where realistic touch sensations are critical for task performance. Traditional haptic systems in VR often rely on predefined interactions or wearables that limit realism and flexibility. Collaborative robots (Cobots), originally developed to work safely alongside humans in industrial environments, offer new opportunities in the context of VR, particularly as “Encountered-Type” haptic devices capable of delivering physical feedback on demand.
This thesis aims to explore the use of Cobots to simulate a wide range of manual interactions by providing adaptive and realistic haptic feedback in VR environments. In particular, the focus will be on the design and development of a custom, shape-changing robotic flange capable of simulating different surface geometries, such as flat, curved, or edged surfaces, through mechanical adaptation. The use of actuators, motors, and modular 3D-printed components will enable the system to dynamically adjust its shape in real time based on user interaction and task requirements.
The system will be integrated into a VR setup where users perform tasks requiring diverse tactile responses, such as object inspection, surface tracing, or precision alignment. A dedicated control framework will be developed to synchronize robot motion and flange adaptation with virtual content. The system’s performance will be validated through user studies evaluating haptic realism, versatility, and overall immersion across a variety of simulated tasks.
References:
[1] V. K Guda, S. Mugisha, C. Chevallereau, and D. Chablat, “Introduction of a Cobot as Intermittent Haptic Contact Interfaces in Virtual Reality”. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2023. Lecture Notes in Computer Science, vol 14028. Springer, Cham. doi: 10.1007/978-3-031-35741-1_36

Problem:
Traditional Virtual Tours often lack real-time responsiveness to user behavior. Virtual guides often fail to ensure group cohesion or tailor content dynamically, leading to disengagement and fragmented experiences.
Objective:
To develop AI-powered tools capable of tracking user behavior, maintaining group coherence, and adapting content delivery to individual and collective engagement patterns in digital museum and showroom tours.
Approach: The system will integrate real-time user tracking, behavioral modeling, either to adapt content presentation or to offer valuable hint and suggestion to the designated guide. Machine learning algorithms will infer user intent and attention, enabling the guide to adjust pacing, highlight relevant artifacts, and ensure no participant is left behind.
Expected Impact:
This research is expected to improve user engagement and retention in digital cultural experiences by enabling intelligent, behavior-aware guidance. It will support more inclusive and personalized tours, where virtual curators
adapt dynamically to group cohesion and individual interests. The outcomes may inform future design strategies for scalable, AI-enhanced museum and showroom applications.