Performing complex tasks in open environments remains challenging for robots, even when using large language models (LLMs) as the core planner. Many LLM-based planners are inefficient due to their large number of parameters and prone to inaccuracies because they operate in open-loop systems. We think the reason is that only applying LLMs as planners is insufficient. In this work, we propose DaDu-E, a robust closed-loop planning framework for embodied AI robots. Specifically, DaDu-E is equipped with a relatively lightweight LLM, a set of encapsulated robot skill instructions, a robust feedback system, and memory augmentation. Together, these components enable DaDu-E to (i) actively perceive and adapt to dynamic environments, (ii) optimize computational costs while maintaining high performance, and (iii) recover from execution failures using its memory and feedback mechanisms. Extensive experiments on real-world and simulated tasks show that DaDu-E achieves task success rates comparable to embodied AI robots with larger models as planners like COME-Robot, while reducing computational requirements by $6.6 \times$. Users are encouraged to explore our system at: \url{https://rlc-lab.github.io/dadu-e/}.

翻译：在开放环境中执行复杂任务对于机器人而言仍然具有挑战性，即使使用大语言模型作为核心规划器也是如此。许多基于LLM的规划器因其庞大的参数量而效率低下，并且由于它们在开环系统中运行而容易产生不准确的结果。我们认为，仅将LLM用作规划器是不够的。在这项工作中，我们提出了DaDu-E，一个用于具身AI机器人的鲁棒闭环规划框架。具体而言，DaDu-E配备了一个相对轻量级的LLM、一套封装的机器人技能指令、一个鲁棒的反馈系统以及记忆增强模块。这些组件共同使DaDu-E能够：(i) 主动感知并适应动态环境，(ii) 在保持高性能的同时优化计算成本，以及 (iii) 利用其记忆和反馈机制从执行失败中恢复。在真实世界和模拟任务上进行的大量实验表明，DaDu-E实现了与使用更大模型（如COME-Robot）作为规划器的具身AI机器人相当的任务成功率，同时将计算需求降低了 $6.6 \times$。我们鼓励用户通过以下网址探索我们的系统：\url{https://rlc-lab.github.io/dadu-e/}。