At your Command! An Empirical Study on How LaypersonsTeach Robots New Functions

TL;DR

This study empirically investigates how laypersons teach robots new functions using natural language, revealing common patterns and challenges, and providing a labeled corpus to advance user-robot interaction research.

Contribution

It presents a large, publicly available corpus of natural language instructions and analyzes linguistic patterns in layperson-robot teaching interactions.

Findings

Many participants used specific wordings to express teaching intent

Over one third of utterances did not verbalize teaching intent

The corpus includes labeled semantic constituents of instructions

Abstract

Even though intelligent systems such as Siri or Google Assistant are enjoyable (and useful) dialog partners, users can only access predefined functionality. Enabling end-users to extend the functionality of intelligent systems will be the next big thing. To promote research in this area we carried out an empirical study on how laypersons teach robots new functions by means of natural language instructions. The result is a labeled corpus consisting of 3168 submissions given by 870 subjects. The analysis of the dataset revealed that many participants used certain wordings to express their wish to teach new functionality; two corresponding trigrams are among the most frequent. On the contrary, more than one third (36.93%) did not verbalize the teaching intent at all. We labeled the semantic constituents in the utterances: declaration (including the name of the function) and intermediate steps. The full corpus is publicly available: http://dx.doi.org/10.21227/zecn-6c61