As robots increasingly share spaces with people, it becomes important for them to behave according to our social norms.
In this paper, we explore the problem of finding socially acceptable locations for a robot to wait for a shared elevator by learning from expert annotations.
Access to relevant, unlabeled data is however scarce in this setting and annotations expensive to gather, as they require explicit knowledge about the social norms, the robot, and the service it carries out.
We tackle this low-data regime as follows.
First, we use Procedural Content Generation to generate plausible waiting scenes to be annotated.
Second, we leverage available sociological studies and operationalize relevant social norms as feature maps.
We train a variety of models with only 125 procedurally-generated expert-annotated scenes, testing the impact of the proposed feature maps.
In our ablation study, the feature maps help the models’ performance and their generalization capabilities to non-synthetic, real scenes.
We inspect the decisions taken by the best models, probing their strengths and weaknesses, and identifying general issues and discuss potential solutions.
Robots waiting for the elevator: integrating social norms in a low-data regime goal selection problem
| Mattia Racca, Jutta Willamowski, Tommaso Colombino, Gianluca Monaci, Danilo Gallo |
| 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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