Publikation
A Bayesian Approach to Context-based Recognition of Human Intention for Context-Adaptive Robot Assistance in Space Missions
Adrian Auer; Octavio Arriaga; Teena Hassan; Nina Hoyer; Elsa Andrea Kirchner
In: Proceedings of SpaceCHI 2.0 - Human-Computer Interaction for Space Exploration - A Workshop at CHI 2022. SpaceCHI 2.0: Human-Computer Interaction for Space Exploration (SpaceCHI 2.0-2022), located at ACM CHI 2022, May 1 -1, New Orleans, LA, USA, ACM, 5/2022.
Zusammenfassung
Long-duration space exploration (LDSE) missions with a high demand for intelligent and adaptable robotic assistance are expected
to start in the next decades. Robot assistants deployed on these
missions should be able to interpret the intentions of astronaut(s)
and provide assistance accordingly, under continuously changing
interaction context. This necessitates the development of robust and
context-adaptive models for inferring human intentions. However,
current state-of-the-art artificial intelligence (AI) algorithms rely
on expensive data-driven machine learning (ML) models that provide inferences assuming a set of known contexts. In this paper, we
present a context-adaptive hybrid architecture for intelligent and
adaptable Human Computer Interaction (HCI). This architecture
uses state-of-the-art machine learning models for the recognition
of specific contexts within the robot’s environment, and a two-layer
Bayesian network to infer human intentions based on the current
context. The Bayesian network enables the integration of human
expert knowledge about the mission goals within the inference
process and can also deal with missing or uncertain information
about context. Thus, the proposed framework increases robustness
and reduces the need for large and complex (high dimensional)
human-robot interaction data, which is unavailable in space applications. Context-adaptive interpretation of human intention would
promote an intuitive interaction with robots, which in turn could
reduce the overall stress in astronauts while interacting with robot
assistants in isolated, confined or extreme (ICE) conditions.