What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper

TL;DR

This paper introduces an online learning framework enabling social robots like Pepper to learn and adapt to different social norms across various contexts through interaction and active learning.

Contribution

It presents a novel online learning approach for robots to acquire scene-specific norms using incremental learning and Dempster-Schafer theory, demonstrated on Pepper.

Findings

Pepper successfully learned multiple scenes and norms through online interaction.

The framework effectively models context-specific norms with Dempster-Schafer theory.

Active learning improves the efficiency of norm acquisition.

Abstract

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.