Improving HRI through robot architecture transparency

Abstract

In recent years, an increased effort has been invested to improve the capabilities of robots. Nevertheless, human-robot interaction remains a complex field of application where errors occur frequently. The reasons for these errors can primarily be divided into two classes. Foremost, the recent increase in capabilities also widened possible sources of errors on the robot's side. This entails problems in the perception of the world, but also faulty behavior, based on errors in the system. Apart from that, non-expert users frequently have incorrect assumptions about the functionality and limitations of a robotic system. This leads to incompatibilities between the user's behavior and the functioning of the robot's system, causing problems on the robot's side and in the human-robot interaction. While engineers constantly improve the reliability of robots, the user's understanding about robots and their limitations have to be addressed as well. In this work, we investigate ways to improve the understanding about robots. For this, we employ FAMILIAR -FunctionAl user Mental model by Increased LegIbility ARchitecture, a transparent robot architecture with regard to the robot behavior and decision-making process. We conducted an online simulation user study to evaluate two complementary approaches to convey and increase the knowledge about this architecture to non-expert users: a dynamic visualization of the system's processes as well as a visual programming interface. The results of this study reveal that visual programming improves knowledge about the architecture. Furthermore, we show that with increased knowledge about the control architecture of the robot, users were significantly better in reaching the interaction goal. Furthermore, we showed that anthropomorphism may reduce interaction success.

A Literature Survey of How to Convey Transparency in Co-Located Human–Robot Interaction

“…Olatunji et al [29] designed a GUI displayed on a monitor next to the robot to communicate with the robot in visual form, supplemented with reasoning to action, e.g., "I'm bringing the plate as you asked". Researchers who are interested in robot explainability, such as [52,69,80,83,89], tend to use interfaces containing images of the robot, sensor information, colorful highlights, and text, e.g., "using this gripper: left" [83]. Sanders et al [67] used the opportunity of external screens to explore different levels of information on their effect on trust.…”