On-demand adaptive haptic feedback using collaborative robots for manual task simulation in Virtual Reality

Thesis Description & Objectives

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

Advisors

Fabrizio Lamberti

Fabrizio Lamberti

Full Professor, Head of the Group

Davide Calandra

Davide Calandra

Fixed-term Assistant Professor

Lorenzo Valente

Lorenzo Valente

Ph.D. Candidate

Proposal Details

PoliTo Thesis ID #15074
PoliTo Thesis Page →
Application Deadline 07/25/2026
Keywords