Privacy in human–robot interaction (HRI) is commonly enforced through static rules, despite findings that privacy expectations vary across spaces, activities, and social situations. This paper argues that such static treatments could be improve to provide privacy-aware behaviors for robots operating in shared human environments. We propose a conceptual system architecture for treating privacy as a contextual and user-fine-tunable property of robot behavior. Rather than robot autonomously acquiring data, the architecture enables users to specify how data is acquired and how it will be used to reg-ulate navigation, perception, and interaction through fine-tunable, policy-based behavioral constraints. Privacy expectations are spec-ified using natural-language input and interpreted at runtime to adapt robot behavior. Office and Hospital scenarios are presented to illustrate how contextual rules and behavioral interventions can be applied across spaces with different privacy expectations. This work represents a first step toward modular, context-aware privacy frameworks for social robots.
A contextual architecture for privacy-aware robot behavior
| Cherry San, Antonietta Grasso, Adriana Tapus |
| Transparent and Interpretable Robots (TRAIL), Catania, Italy, 16-17 April, 2026 |
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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