Developing autonomous home robots controlled by natural language has long been a pursuit of human. While advancements in large language models (LLMs) and embodied intelligence make this goal closer, several challenges persist: the lack of a unified benchmark for more complex robot tasks, limited evaluation methods and metrics, data incompatibility between LLMs and mobile manipulation trajectories. To address these issues, we introduce Embodied Mobile Manipulation in Open Environments (EMMOE), which requires agents to interpret user instructions and execute long-horizon everyday tasks in continuous space. EMMOE seamlessly integrates high-level and low-level embodied tasks into a unified framework, along with three new metrics for more diverse assessment. Additionally, we collect EMMOE-100, which features in various task attributes, detailed process annotations, re-plans after failures, and two sub-datasets for LLM training. Furthermore, we design HomieBot, a sophisticated agent system consists of LLM with Direct Preference Optimization (DPO), light weighted navigation and manipulation models, and multiple error detection mechanisms. Finally, we demonstrate HomieBot's performance and the evaluation of different models and policies.

本研究解决了当前在复杂机器人任务中缺乏统一基准及评估方法的问题，以推动自然语言控制的自主家庭机器人技术的发展。通过引入EMMOE这一框架，该框架同时整合了高层次和低层次的具身任务，并提供了三种新的评估指标，显著提升了对多样化任务的评估能力。研究的主要发现是，HomieBot展现出卓越的性能，推动了具身移动操控领域的进步。

EMMOE：开放环境中具身移动操作的综合基准