Robots are increasingly present in spaces inhabited by humans. From a privacy-sensitive design perspective, they present challenges, as they acquire data about their environment to act autonomously and interact with their users. This may raise privacy concerns among robot users and bystanders. To address these concerns, we propose a multi-touchpoint design enabling users and bystanders to investigate how their privacy is protected.These touchpoints include (1) embodied interaction with the robot, either directly, whenever encountering a robot, or (2) later, in a dedicated physical space, (3) interaction with a virtual AI chatbot through a web site, and (4) interaction with a human Data Protection Officer.
We evaluated this design and the usefulness of the proposed touchpoints in two studies.Our findings are threefold: first, all touchpoints are useful and complement each other; second, different people have different preferences; and third, the attributes of the situation (i.e. location, busyness, contextuality and sensitivity) impact the choice of the touchpoint they would use to ask their questions.
Designing multi-touchpoint privacy conversations for service robots
| Antonietta Grasso, Jisun Park, Jutta Willamowski |
| The IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Eindhoven, The Netherlands, 25-29 August, 2025 |
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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