Resolving the safety-autonomy paradox: a noise-resilient trust alignment framework for geriatric care robots

In elderly home care, robots face a “safety-autonomy” trust paradox: rigid interventions ensure physical safety but undermine user autonomy. While existing explainable AI (XAI) attempts value alignment, it often assumes perfect perception and ignores the “explanation fatigue” and dynamic cognitive load of elderly users. To address these challenges, we propose the Sequential Trust Alignment framework (STAMM), featuring three core mathematical formulations: (1) a cognitive-modulated dynamic rationality coefficient ( β t ); (2) a confidence-aware recursive Bayesian trust update ( α t ) with a forgetting factor to mitigate multimodal sensory noise; and (3) a dynamic explanation fatigue threshold ( τ t ) ensuring “no disturbance unless necessary.” We evaluated STAMM using a dual-dimensional approach: embodied physical simulations in AI2-THOR under complex noise, and a Human-in-the-Loop (HITL) empirical study across high-risk geriatric scenarios. Results show that STAMM effectively alleviates the tension along the safety-autonomy trade-off, maintaining a high physical safety rate (97.5%) while significantly reducing intervention rejections. Furthermore, the HITL study demonstrates that STAMM achieves significantly higher Overall Trust and Perceived Benevolence while reducing Cognitive Load compared to rigid or “Always-On” baselines. By effectively suppressing trust oscillations under perceptual uncertainty, STAMM demonstrates strong robustness, offering an empathetic and practical approach for real-world Human-Robot Interaction.