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Agile assistive hospital robot for suboptimal Task execution in dynamic environments
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DOI：10.1007/s10514-026-10255-6.png)
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Abstract
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Healthcare staff shortages increase workloads and create a need for assistive robots capable of handling routine hospital tasks. Traditional rule-based systems struggle in dynamic environments and with unforeseen requests. We present an agile hospital assistive robot that interprets natural language instructions using an AI-based task planner and keyword retrieval to generate executable task sequences. The robot adapts in real time to additional user requests, reschedules tasks using a suboptimal genetic-algorithm-based approach, and recovers from execution failures with vision-language reasoning and AI suggestions. Deployed on a Temi robot with a custom Android application, the system demonstrates effective handling of dynamic changes, improved human-robot interaction efficiency, and positive feedback from nursing staff.
Keywords:
Socially assistive robots
Hospital robots
Task planning
Suboptimal rescheduling
Large language models
Human-robot interaction
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