What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper
By Ali Ayub, Alan R. Wagner. 2020
14 citations recorded by the source corpus.
Social norms support coordination and cooperation in society. With social robots becoming increasingly involved in our society, they also need to follow the social norms of the society. This paper presents a computational framework for learning contexts and the social norms present in a context in an online manner on a robot. The paper utilizes a recent state-of-the-art approach for incremental learning and adapts it for online learning of scenes (contexts). The paper further utilizes Dempster-Schafer theory to model context-specific norms. After learning the scenes (contexts), we use active learning to learn related norms. We test our approach on the Pepper robot by taking it through different scene locations. Our results show that Pepper can learn different scenes and related norms simply by communicating with a human partner in an online manner.
Subfields: Artificial Intelligence, Control and Systems Engineering, Social Psychology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ali Ayub
- C. Fendley
- Alan R. Wagner
- Tyler L. Hayes
- Christopher Kanan
- Chrystopher L. Nehaniv
- Kerstin Dautenhahn
In Control and Systems Engineering
In Social Psychology
Citations in the corpus
- Few-Shot Continual Active Learning by a Robot (2022, background): “In contrast, robots operating in real-world environments mostly have to learn from supervision provided by their users [11, 5, 12].”
- Online Learning of Objects through Curiosity-Driven Active Learning (2021, background): “To overcome catastrophic forgetting, many different deep learning techniques have been proposed for continual learning [12], [13], [14], [15], [16], [17], [18].”
- Learning Novel Objects Continually Through Curiosity (2021, methodology): “We analyze our previously proposed approach for continual learning termed Centroid-Based Concept Learning with Pseudorehearsal (CBCL-PR) [15], [18], [19] and show that the cluster representations learned from the previous data can be used to learn unseen objects through curiosity.”