Publication

A Holistic Approach for Enhancing Data Integrity and Reliability in Human-Robot Interaction

Manuel Meder, Kashmira Shinde, Dennis Hemker, Sadique Adnan Siddiqui, Teena Hassan, Nina Hoyer, Elsa Andrea Kirchner

In: Proceedings of SpaceCHI 2.0: Human-Computer Interaction for Space Exploration. SpaceCHI 2.0: Human-Computer Interaction for Space Exploration (SpaceCHI 2.0-2022) A Workshop at ACM CHI 2022 May 1-1 New Orleans, LA United States ACM 5/2022.

Abstract

Space is a remote and hostile environment, and it poses several challenges to both, humans and robots deployed in space missions. This necessitates the use of robotic systems on which humans can rely on. However, in extreme environments, sensors might fail or deliver incorrect measurements. Furthermore, signal noise and model inaccuracies could result in inconsistencies between information extracted using different sensors or algorithms. Erroneous and unreliable data can cause autonomous systems to behave in unexpected or inappropriate ways, which reduces human trust in such systems. This in turn would hamper the assistive potential of robots in space missions. Methods that identify and handle sensor failures and lowlevel data inconsistencies are commonly applied to strengthen the system’s robustness. However, few have addressed logical inconsistencies that occur at higher semantic levels of data processing. By following a holistic approach that checks for, and handles both types of inconsistencies, we can enhance the integrity of the data that is used for higher-level inferences and decision-making by robots. This would make robots a more reliable interaction partner for humans. Therefore, we propose an architecture to detect and respond to inconsistencies at both the local and the global levels of data processing. A first implementation of the proposed approach includes auxiliary methods to perform preliminary checks and to facilitate integration into a robot’s dataflow architecture. The proposed architecture could also be applied to terrestrial use cases (e.g., in healthcare), where data integrity, reliability, and trust play a crucial role.

Projekte

20220408_consistency_management_meder_shinde_hemker.pdf (pdf, 473 KB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz