Hybrid Control Strategies for Safe and Adaptive Robot-Assisted Dressing

TL;DR

This paper introduces hybrid control strategies for robot-assisted dressing that enhance safety and adaptability by integrating force monitoring, user feedback, and autonomous adjustments in real-time.

Contribution

It presents novel hazard-driven low-level control mechanisms that improve safety and task success in robot-assisted dressing through real-time user feedback and autonomous interventions.

Findings

Force monitoring combined with user feedback enhances safety.

Hybrid control strategies improve task continuity.

Real-time adaptability increases user trust.

Abstract

Safety, reliability, and user trust are crucial in human-robot interaction (HRI) where the robots must address hazards in real-time. This study presents hazard driven low-level control strategies implemented in robot-assisted dressing (RAD) scenarios where hazards like garment snags and user discomfort in real-time can affect task performance and user safety. The proposed control mechanisms include: (1) Garment Snagging Control Strategy, which detects excessive forces and either seeks user intervention via a chatbot or autonomously adjusts its trajectory, and (2) User Discomfort/Pain Mitigation Strategy, which dynamically reduces velocity based on user feedback and aborts the task if necessary. We used physical dressing trials in order to evaluate these control strategies. Results confirm that integrating force monitoring with user feedback improves safety and task continuity. The findings emphasise the need for hybrid approaches that balance autonomous intervention, user involvement, and controlled task termination, supported by bi-directional interaction and real-time user-driven adaptability, paving the way for more responsive and personalised HRI systems.