As robots become more and more common in human environments, ensuring privacy-aware robot navigation becomes essential. Various methods have been proposed to address privacy, such as desensitizing, but they do not enable a fine grained contextual adaptation to a specific deployment site. Our ongoing work explores methods that enable service owners of office robots to fine tune and dynamically adjust robot navigation behavior to respect contextual privacy. We propose an end-user interface that integrates a multimodal Large Language Model (LLM), allowing users to define, refine, and enforce predefined privacy rules. In this paper we define generic office privacy rules and the reasoning required to make them contextual. These mechanisms enable robots to anticipate privacy-sensitive situations using time, location, service type, and human presence as contextual factors.
Contextual dependency of office privacy
| Antonietta Grasso, Jisun Park, Jutta Willamowski |
| Privacy-Sensitive Robotics: Perceptions, Measures, and Metrics workshop at the 20th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI), Melbourne, Australia, 3 March, 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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