Authoring human simulators via probabilistic functional reactive program synthesis

Abstract

One of the core challenges in creating interactive behaviors for social robots is testing. Programs implementing the interactive behaviors require real humans to test and this requirement makes testing of the programs extremely expensive. To address this problem, human-robot interaction researchers in the past proposed using human simulators. However, human simulators are tedious to set up and context-dependent and therefore are not widely used in practice. We propose a program synthesis approach to building human simulators for the purpose of testing interactive robot programs. Our key ideas are (1) rep-resenting human simulators as probabilistic functional reactive programming programs and (2) using probabilistic inference for synthesizing human simulator programs. Programmers then will be able to build human simulators by providing interaction traces between a robot and a human or two humans which they can later use to test interactive robot programs and improve or tweak as needed.

BibTeX Entry

@inproceedings{chung2022authoring,
  title = {Authoring human simulators via probabilistic functional reactive program synthesis},
  author = {Chung, Michael Jae-Yoon and Cakmak, Maya},
  year = {2022},
  booktitle = {ACM/IEEE International Conference on Human-Robot Interaction (HRI) - Late breaking reports},
  pages = {727--730},
  organization = {IEEE},
  type = {late-breaking},
  doi = {10.1109/HRI53351.2022.9889630}
}