Haptics with Open Source: From Vision to Touch

Touch is one of the most important human senses, yet it remains largely absent from the open source ecosystem. While the FOSS community has built extraordinary tools for vision, audio, graphics and machine learning, very little attention has been given to making tactile perception accessible, reproducible and open.

This talk introduces a vision to touch pipeline built using open-source tools and research methods. Starting from an image of a surface, we explore how texture features can be extracted using Python, OpenCV and scientific computing libraries, and how these features can be mapped to human perception of roughness and texture. Drawing from research in psychophysics, haptics and human-machine interaction, the session demonstrates how visual information can be transformed into tactile representations.

The talk will include examples of texture analysis, perceptual modelling and a live demonstration showing how different surface textures can be characterised and interpreted using open-source software. Beyond the technical aspects, the session proposes an open-science approach to haptics. Attendees will learn how open datasets, open-source software and reproducible research practices can accelerate innovation in tactile interfaces, assistive technologies, robotics and human-computer interaction.

The goal is simple: if open source helped computers see, perhaps the next frontier is helping computers feel.

1. Understand the fundamentals of human texture perception and haptic interaction.

2. Learn how texture information can be extracted from images using open-source tools.

3. Explore the relationship between computer vision, psychophysics and tactile perception.

4. See how Python, OpenCV and scientific computing libraries can be used to build vision to touch pipelines.

5. Discover opportunities for open source contributions in haptics, tactile interfaces and open science.

6. Learn how open datasets and reproducible research can accelerate innovation in human-machine interaction.

7. Gain insight into future applications in robotics, accessibility, virtual reality and assistive technologies.