We introduce an interactive LLM-based framework designed to enhance the autonomy and robustness of domestic robots, targeting embodied intelligence. Our approach reduces reliance on large-scale data and incorporates a robot-agnostic pipeline that embodies an LLM. Our framework, InteLiPlan, ensures that the LLM's decision-making capabilities are effectively aligned with robotic functions, enhancing operational robustness and adaptability, while our human-in-the-loop mechanism allows for real-time human intervention when user instruction is required. We evaluate our method in both simulation and on the real Toyota Human Support Robot (HSR). Our method achieves a 93% success rate in the 'fetch me' task completion with failure recovery, highlighting its capability in both failure reasoning and task planning. InteLiPlan achieves comparable performance to state-of-the-art large-scale LLM-based robotics planners, while using only real-time onboard computing.
View on arXiv@article{ly2025_2409.14506,
title={ InteLiPlan: An Interactive Lightweight LLM-Based Planner for Domestic Robot Autonomy },
author={ Kim Tien Ly and Kai Lu and Ioannis Havoutis },
journal={arXiv preprint arXiv:2409.14506},
year={ 2025 }
}