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 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. InteLiPlan achieves comparable performance to state-of-the-art LLM-based robotics planners, while using only real-time onboard computing. Project website: https://kimtienly.github.io/InteLiPlan.

翻译：本文提出了一种基于交互式大语言模型的框架，旨在增强家用机器人的自主性与鲁棒性，以推进具身智能的发展。该方法降低了对大规模数据的依赖，并采用了一种与机器人平台无关的、内嵌大语言模型的流程。我们的框架InteLiPlan确保了大语言模型的决策能力与机器人功能有效对齐，从而提升了操作的鲁棒性和适应性；同时，其中的人机协同机制允许在需要用户指令时进行实时的人工干预。我们在仿真环境和真实机器人平台（包括丰田人机协作机器人以及搭载Unitree Z1机械臂的ANYmal D机器人）上对该方法进行了评估。在具备故障恢复功能的“取物”任务中，我们的方法实现了95%的成功率，突显了其在故障推理与任务规划两方面的能力。InteLiPlan仅使用实时机载计算，其性能即可与当前最先进的基于大语言模型的机器人规划器相媲美。项目网站：https://kimtienly.github.io/InteLiPlan。