Davide Calandra

Cybersickness is a disorder characterized by symptoms such as nausea or discomfort that can occur during or after the use of Virtual Reality (VR) technologies. Similarly to motion sickness, it is mainly caused by inconsistencies between visual and auditory stimuli from the simulated environment and the expected feedback from the vestibular system. Several mitigation and prevention techniques have been proposed and are employed in various commercial products. However, most of them are static, highly situational, or require design choices that negatively affect key aspects of the VR experience such as immersion, naturalness, and sense of presence.
A promising direction that remains underexplored is the use of adaptive mitigation techniques that respond dynamically to the user’s real-time state. Recent advances in physiological monitoring and data-driven modeling make it possible to estimate cybersickness symptoms during VR exposure by analyzing signals such as heart rate or skin conductance, as well as behavioral data like movement patterns and interaction metrics. These indicators, when interpreted using machine learning or rule-based models, offer the opportunity to adapt the virtual environment continuously in response to the user’s current level of discomfort.
The objective of this thesis is to design and implement an adaptive mitigation system capable of modifying VR parameters in real time based on continuous detection of cybersickness indicators. The system will be integrated into an existing VR environment and will be tested by adjusting elements such as motion intensity, visual effects, or field of view in order to alleviate symptoms without significantly compromising immersion. The effectiveness of the approach will be evaluated through a user study, combining subjective and objective measures to assess comfort, presence, and user experience over time.
References:
– Jyotirmay Nag Setu et al., Mazed and Confused: A Dataset of Cybersickness, Working Memory, Mental Load, Physical Load, and Attention During a Real Walking Task in VR, 2024 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 10.1109/ISMAR62088.2024.00121
– Davide Calandra, Fabrizio Lamberti, A Testbed for Studying Cybersickness and its Mitigation in Immersive Virtual Reality, IEEE Transactions on Visualization and Computer Graphics ( Volume: 30, Issue: 12, December 2024), 10.1109/TVCG.2024.3448203
– Rifatul Islam et al., Cybersickness Prediction from Integrated HMD’s Sensors: A Multimodal Deep Fusion Approach using Eye-tracking and Head-tracking Data, 2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 10.1109/ISMAR52148.2021.00017

Recent advances in Generative Artificial Intelligence are opening new possibilities for the design and development of immersive Virtual Reality (VR) learning experiences. Traditional VR authoring pipelines rely heavily on manual processes, including 3D modelling, scripting, and scenario design, which limit scalability, adaptability, and responsiveness to evolving training needs. At the same time, generative AI techniques are increasingly being integrated into VR systems to automate the creation of environments, assets, and interactions, enabling more dynamic and personalized experiences.
This thesis aims to investigate the integration of Generative AI into VR-based training systems, with the objective of automating the creation of virtual environments, interactive objects, and narrative logic. The work will explore how textual or semantic inputs can be leveraged to generate coherent VR experiences by combining generative models with procedural content generation techniques. In particular, hybrid approaches will be considered, where text prompting guides the generation process while procedural systems enforce constraints, consistency, and scalability of the resulting environments.
The student will design and develop prototype systems supporting semi-automatic or fully automatic authoring of VR training scenarios. This includes the integration of large language models for scenario generation, generative models for 3D asset creation, and procedural methods for assembling environments and interactions. Particular attention will be devoted to the interaction between generative and procedural components, the definition of intermediate representations, and the design of prompting strategies for controllable and reliable generation.
The developed systems will be evaluated through both qualitative and quantitative analyses, considering factors such as generation quality, coherence of the resulting experiences, adaptability to different training contexts, and usability. The expected outcome is a reduction in development time and cost for immersive training applications, along with contributions toward a structured framework for integrating Generative AI and procedural techniques into VR authoring pipelines, supporting scalable and adaptive training systems.
References:
[1] Y. Song, K. Wu, and J. Ding, “Developing an immersive game-based learning platform with generative artificial intelligence and virtual reality technologies – “LearningverseVR,” in Computers & Education: X Reality 4, 2024, pp. 1-7. doi: 10.1016/j.cexr.2024.100069.
[2] X. Mao, “Procedural Content Generation via Generative Artificial Intelligence,” arXiv:2407.09013, 2024, pp. 1-15, doi: 10.48550/arXiv.2407.09013.
[3] F. Rahimi, A. Sadeghi-Niaraki, and S. Choi “Generative AI Meets Virtual Reality: A Comprehensive Survey on Applications, Challenges, and Future Direction,” in IEEE Access, vol. 13, pp. 94893-94909, 2025, doi: 10.1109/ACCESS.2025.3574779.

Virtual Reality (VR) has proven effective in simulating procedural tasks and structured training scenarios, particularly in domains where experiential learning is critical. However, current VR learning experiences are often limited in their ability to support dynamic problem-solving, especially in situations where learners are required to devise tools, strategies, or solutions in real time. This lack of adaptability reduces learner agency and constrains the exploration of alternative approaches within immersive environments.
This thesis aims to investigate the use of Generative Artificial Intelligence to enhance learner agency in VR-based training systems. The objective is to enable users to interactively solve training scenarios by leveraging AI to generate context-aware suggestions, including tools, hypotheses, and procedural steps. In addition, the work will explore the integration of Reinforcement Learning techniques to adapt the behavior of the AI assistant over time, optimizing when and how support is provided based on user interactions and learning progress.
The student will design and develop prototype systems in which users can invoke AI assistance during VR training sessions. The approach will initially focus on unconstrained environments, where learners can freely explore AI-generated solutions in a sandbox setting. Subsequently, domain-specific constraints will be introduced to guide the generation process toward feasible and coherent solutions aligned with the procedural logic of the task. Reinforcement Learning strategies will be investigated to model adaptive assistance policies, allowing the system to personalize feedback, regulate the level of guidance, and improve its effectiveness through interaction. Particular attention will be devoted to balancing flexibility and control, as well as to the definition of meaningful reward signals based on user behavior, task completion, and learning outcomes.
The developed systems will be evaluated through qualitative and quantitative methods, considering aspects such as user engagement, perceived agency, solution diversity, coherence with domain knowledge, and overall usability. The expected outcome is an enhancement of learner creativity and autonomy through AI-assisted problem solving, along with the development of adaptive learning pathways tailored to user behavior. This work may also contribute to the definition of intelligent, responsive training environments capable of evolving in real time through the combined use of Generative AI and Reinforcement Learning.
References
[1] S. Doroudi, V. Aleven, and E. Brunskill, “Where’s the Reward? A Review of Reinforcement Learning for Instructional Sequencing,” in International Journal of Artificial Intelligence in Education 29, 2019, pp. 568-620. doi: 10.1007/s40593-019-00187-x.
[2] S. Yao et al. “ReAct: Synergizing Reasoning and Acting in Language Models,” arXiv:1907.00456, 2019, pp. 1-16, doi: 10.48550/arXiv.1907.00456.
[2] N. Jaques et al. “Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog,” arXiv:2210.03629, 2023, pp. 1-33, doi: 10.48550/arXiv.2210.03629.

As robots become increasingly present in industrial and collaborative environments, effective human-robot interaction (HRI) is emerging as a critical challenge. While industrial robots have reached high levels of precision and efficiency, they are still primarily designed with a strong focus on functionality and automation, with limited attention to social and communicative aspects of interaction. As a consequence, robots are often perceived as intimidating, unpredictable, and emotionally distant, which can reduce user trust, increase anxiety, and negatively affect collaboration in shared workspaces.
A promising direction to address these limitations is the integration of human-like social and communicative behaviors into robotic systems. In human-human interaction, non-verbal cues such as movement expressivity, gaze direction, and feedback signals play a fundamental role in conveying intention and improving predictability. Introducing similar mechanisms in industrial robots could make their behavior easier to interpret, fostering a sense of safety and improving the overall interaction experience. Despite growing interest in social robotics, the application of these concepts in industrial contexts remains relatively underexplored, especially in scenarios requiring real-time adaptation to the user.
The objective of this thesis is to design and implement an adaptive interaction system for a collaborative industrial robot, capable of modifying its behavior dynamically based on the ongoing interaction with the human user. The system will leverage multimodal signals such as movement patterns, spatial proximity, and interaction dynamics to adjust aspects of the robot’s behavior, including motion expressivity, communicative feedback, and interaction timing. The goal is to create interactions that feel more natural, predictable, and aligned with human expectations, without compromising task efficiency.
The proposed solution will be integrated into a collaborative robotic platform and evaluated through a user study. Both subjective and objective measures will be considered to assess the effectiveness of the approach, including perceived trust, safety, user comfort, and task performance. The study will compare adaptive and non-adaptive behaviors to understand the impact of socially aware interaction strategies on human-robot collaboration.
This research is expected to contribute to the development of socially intelligent industrial robots capable of improving trust, reducing anxiety, and enabling more effective and intuitive collaboration. The results may have implications for a wide range of applications, including manufacturing, logistics, and training environments, where humans and robots increasingly operate side by side.
References:
[1] Linda Onnasch and Clara Laudine Hildebrandt. 2021. Impact of Anthropomorphic Robot Design on Trust and Attention in Industrial Human-Robot Interaction. J. Hum.-Robot Interact. 11, 1, Article 2 (March 2022), 24 pages. https://doi.org/10.1145/3472224
[2] Naendrup-Poell, L., Onnasch, L. Predictive robot eyes shape visual attention, performance, and trust in interaction with an industrial CoBot. Sci Rep 16, 14171 (2026). https://doi.org/10.1038/s41598-026-50476-4
[3] Esmeralda Faria, Ana Pinto, Soraia Oliveira, Gustavo Assunção, Carla Carvalho, Paulo Menezes, Collaborative robots and user trust: The role of saccadic gaze, anthropomorphic motion, and repetitive training, Computers in Human Behavior Reports, Volume 21, 2026, 100938, ISSN 2451-9588, https://doi.org/10.1016/j.chbr.2026.100938
[4] Jessup, S.A., Alarcon, G.M., Harris, K.N. et al. The Influence of Robot Anthropomorphism and Trust Violation Types on Trustworthiness Perceptions and Trust Behaviors. Int J of Soc Robotics 17, 1437–1452 (2025). https://doi.org/10.1007/s12369-025-01295-6

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

Extended Reality (XR) technologies have become powerful platforms for immersive training, simulation, and entertainment. However, the quality and naturalness of user interaction remain constrained by tracking inaccuracies, hardware limitations, and individual differences in perception and comfort. These factors can lead to reduced precision, increased effort, or discomfort phenomena such as cyber-sickness, ultimately diminishing immersion and presence.
Recent advances in artificial intelligence and machine learning enable predictive approaches that analyze multimodal data to understand and anticipate user behavior. By processing inputs such as eye gaze, hand motion, physiological signals, application context, and user actions, AI models can infer interaction intent and detect variations in cognitive or physical state in real time. This predictive understanding allows the system to refine interactions dynamically, compensate for inaccuracies, and adapt both the behavior and appearance of interactive elements according to the user’s condition and task, resulting in a more stable and personalized experience.
This thesis aims to explore interaction refinement in XR through AI-based predictions of user state, interaction intent, and contextual conditions. Depending on the research direction, the focus may involve predictive enhancement of fine manipulation and control, or adaptive adjustment of interactive representations to mitigate discomfort and sustain immersion. The system will combine multimodal data from eye and hand tracking, user actions, and, when available, physiological sensors within an experimental XR testbed designed to evaluate adaptive interaction performance.
The research will include the design, implementation, and evaluation of an AI-driven framework for real-time interaction refinement. User studies will assess its impact on accuracy, comfort, and perceived presence. By leveraging AI to anticipate user intent and dynamically adjust both interaction and representation, this work aims to advance the development of intelligent, user-aware XR systems that provide natural, effective, and comfortable experiences across diverse scenarios.
References:
D. Calandra and F. Lamberti, “A Testbed for Studying Cybersickness and its Mitigation in Immersive Virtual Reality,” IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 12, pp. 7788–7805, Dec. 2024.
R. Islam, Y. Lee, M. Jaloli, I. Muhammad, D. Zhu and J. Quarles, “Automatic Detection of Cybersickness from Physiological Signal in a Virtual Roller Coaster Simulation,” IEEE VRW, 2020, pp. 648–649.
M. P. Olsson et al., “Predicting User Intent in Virtual Reality for Fine Manipulation Tasks,” Proceedings of the ACM Symposium on Spatial User Interaction, 2023.
D. A. Bowman et al., “Evaluation of Techniques for Predictive Interaction in Virtual Environments,” Proceedings of CHI, 2016.

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

The integration of Large Language Models (LLMs) with Virtual Reality (VR) offers promising opportunities to redefine how users interact with immersive environments. This thesis explores the use of LLMs to support more natural, intuitive, and hands-free interactions in VR, moving beyond traditional input methods like physical controllers.
The primary goal is to design and implement a framework that leverages the language understanding capabilities of LLMs to enable voice-driven interactions within VR environments. Potential use cases include hands-free locomotion (e.g., teleportation or virtual walking via spoken commands), interaction with virtual objects or interfaces through natural language, and the integration of multimodal LLMs to interpret visual inputs, such as referencing objects in the environment through verbal cues.
The work will involve defining the system architecture, developing prototypes using Unity and voice processing tools, and exploring how LLMs can interpret and respond to user input in real time. The system will be evaluated through user studies, collecting both quantitative and qualitative data to assess usability, command recognition accuracy, response times, and overall user experience.
References:
[1] D. Calandra, F. G. Pratticò, and F. Lamberti, “Comparison of Hands-Free Speech-Based Navigation Techniques for Virtual Reality Training,” in 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), Palermo, Italy, 2022, pp. 85-90. doi: 10.1109/MELECON53508.2022.9842994.
[2] J. A. V. Fernandez, J. J. Lee, S. A. S. Vacca, A. Magana, B. Benes, and V. Popescu, “Hands-Free VR,” arXiv preprint arXiv:2402.15083, 2024
[3] S. Özdel, K. B. Buldu, E. Kasneci, E. Bozkir “Exploring Context-aware and LLM-driven Locomotion for Immersive Virtual Reality” arXiv preprint arXiv:2504.17331, 2025