InteLiPlan: An Interactive Lightweight LLM-Based Planner for Domestic Robot Autonomy

Abstract

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, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">InteLiPlan</i>, 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 robot platforms, including a Toyota Human Support Robot and an ANYmal D robot with a Unitree Z1 arm. Our method achieves a 95% success rate in the ‘fetch me’ task completion with failure recovery, highlighting its capability in both failure reasoning and task planning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">InteLiPlan</i> achieves comparable performance to state-of-the-art LLM-based robotics planners, while using only real-time onboard computing. Project website: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://kimtienly.github.io/InteLiPlan</uri>.