Continuous Real-time Adaptation Framework for Enhancing Trust and Technology Acceptance: An Assistive Feeding Study

摘要

Assistive feeding robots operate in close proximity to users often unfamiliar with robotic systems, making trust a crucial element of Human-Robot Interaction (HRI). While technical challenges in assistive feeding are actively researched, human factors such as trust remain underexplored. We propose the Continuous Real-time Adaptation Framework for Trust and Technology Acceptance (CRAFTT), which adapts the robot’s behaviour in real-time based on user reactions to enhance trust and technology acceptance, thereby giving the user greater control over the interaction. This adaptation is modelled as a multi-objective optimisation problem, minimising emotional and physical discomfort while maximising efficiency. In a within-participants study, 26 participants interacted with both adaptive (CRAFTT-equipped) and non-adaptive robot behaviours during a simulated meal. Dependent variables included user comfort cost and time efficiency, along with self-reported HRI metrics—trust, reliance intention, perceived safety, fluency, technology acceptance and cognitive workload—assessed using validated questionnaires. The robot’s impact was evaluated via Bayesian Data Analysis. Findings indicate that CRAFTT enhances trust, reliance, technology acceptance and fluency, with minimal efficiency loss, while maintaining perceived safety and cognitive workload. Future work will explore long-term interactions, expand the problem space (e.g., real food, multi-arm coordination) and apply CRAFTT to other assistive scenarios such as dressing and bathing.