We present a control framework for robot-assisted dressing that augments
low-level hazard response with runtime monitoring and formal verification. A
parametric discrete-time Markov chain (pDTMC) models the dressing process,
while Bayesian inference dynamically updates this pDTMC’s transition
probabilities based on sensory and user feedback. Safety constraints from
hazard analysis are expressed in probabilistic computation tree logic, and
symbolically verified using a probabilistic model checker. We evaluate
reachability, cost, and reward trade-offs for garment-snag mitigation and
escalation, enabling real-time adaptation. Our approach provides a formal yet
lightweight foundation for safety-aware, explainable robotic assistance.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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