Abstract
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Considerations about social norms compliance in a shared elevator scenario
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| Danilo Gallo, Shreepriya Shreepriya, Tommaso Colombino, Antonietta Grasso, Cécile Boulard |
| RO-MAN 2021 Workshop on Robot Behavior Adaptation to Human Social Norms (TSAR), virtual event, 12 August 2021 |
In this position paper, we present our ongoing research on socially acceptable robot navigation for an indoor elevator sharing scenario. We highlight the current challenge of designing interactions for a robot behavior both effective in accomplishing its tasks, but not intrusive and at risk of breakdown. We discuss the advantages and limitations of modelling these behaviors on the base of a full humanlike approach. In particular, we discuss the risk that a full human-like approach presents of creating the illusion of social competence. It has been observed that this illusion often leads to breakdowns when the technology is faced with complex and potentially ambiguous social situations. We propose the principle of “machine-like yet humanfriendly” behavior as a way to address the risks of the full human mimicking approach. We believe that this approach can provide more understandable and less disruptive behaviors for routine integration into human spaces. We conclude discussing the need for a multi-layer experiment set-up to evaluate and validate this approach.
Related Content
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.
VISION
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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