Project Abstract

Trust is a cornerstone of successful human-robot interaction (HRI), profoundly influencing user acceptance, task performance, and collaboration. As robots become increasingly integrated into various domains of human life, understanding the dynamics of trust between humans and robots becomes paramount.The concept of trust in HRI encompasses the reliance, confidence, and willingness of humans to depend on robots in various tasks and contexts. Trust is multifaceted, influenced by factors such as robot capabilities, reliability, transparency, and perceived intent. Moreover, trust in HRI is dynamic, evolving over time through interaction and experience.Measurement of trust in HRI involves the utilization of diverse frameworks, methodologies, and metrics. Frameworks such as the trust calibration framework and socio-cognitive model offer conceptual lenses for understanding trust formation and maintenance. Methodologies range from subjective measures such as questionnaires to objective measures such as task performance and physiological signals.Challenges persist in the measurement of trust in HRI, including the subjective nature of trust, contextual dependency, and the need for standardized assessment tools. Future directions include the development of real-time trust estimation algorithms, integration of multimodal data for comprehensive assessment, and exploration of cultural influences on trust.In conclusion, trust in HRI is a complex and essential phenomenon that underpins effective human-robot collaboration. By advancing our understanding and measurement of trust, we can foster the development of more trustworthy and resilient robotic systems, ultimately enhancing their integration into human-centric environments.

Keywords: Data Science, Artificial Intelligence, Computer Science, Robot Engineering, Human-Robot Interaction, Machine learning, Algorithms,

 Conference Details

Session: Presentation Stream 21 at Presentation Slot 6

Location: GH022 at Wednesday 8th 09:00 – 12:30

Markers: C�cilia Pradic, Joe Macinnes

Course: MSc Data Science, Masters PG

Future Plans: I’m looking for work