Symbolic Runtime Verification and Adaptive Decision-Making for Robot-Assisted Dressing

2025/04/22 by Yasmin Rafiq, Gricel Vázquez, Rafiq, Yasmin +7 · 2 citations
Engineering · #Advanced machining processes and optimization #FOS: Computer and information sciences #Manufacturing Process and Optimization #Robot Manipulation and Learning #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2504.15666

openalex publication_date 2025/04/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Verification and External Parameter Inference for Stochastic World Models
• Bayesian Learning for the Robust Verification of Autonomous Robots
• Assistive Gym: A Physics Simulation Framework for Assistive Robotics
• Probabilistic Model Checking of Robots Deployed in Extreme Environments

Related

• Adaptive Shielding for Safe Reinforcement Learning under Hidden-Parameter Dynamics Shifts
• Safe Uncertainty-Aware Learning of Robotic Suturing
• Automated Generation of MDPs Using Logic Programming and LLMs for Robotic Applications
• GUARD: Toward a Compromise between Traditional Control and Learning for Safe Robot Systems
• Uncertainty-aware Contact-safe Model-based Reinforcement Learning