Abstract
The emergence of companion robots is promising to alleviate loneliness and improve mental health. It is critical to develop accurate task plans attuned to the various emotional states of a human partner. Given the complexity and variability inherent in human mental states, manually creating plans for companion robots is not feasible. Recent framework that integrates Large Language Models (LLMs) with Planning Domain Definition Language (PDDL) for automated task planning produces precise and flexible task plans. However, this framework has not been applied to companion robots, especially those responding to emotional states. This work introduces a new task planning strategy utilizing LLM and PDDL for companion robots. Simulation results demonstrate that the proposed method enables the robot to successfully navigate and offer support in response to detected states of sadness emotion. The method can convert unstructured natural language descriptions into structured task planning information. This strategy may enhance the interaction quality of companion robots and make them more empathetic and contextually aware in their social support roles.