EMMOE：开放环境中具身移动操作的综合基准
EMMOE: A Comprehensive Benchmark for Embodied Mobile Manipulation in Open Environments
AI总结：
本文提出EMMOE统一基准与配套数据集，评估自然语言智能体在开放环境中的长程移动操作能力，并构建结合DPO训练LLM、轻量模型和错误检测的~\model系统。
摘要：
开发由自然语言控制的自主家用机器人长期以来一直是人类的追求。尽管大型语言模型（LLM）和具身智能的进展使这一目标更加接近，但仍存在若干挑战：缺乏面向更复杂机器人任务的统一基准、评估方法和指标有限，以及LLM与移动操作轨迹之间的数据不兼容。为解决这些问题，我们提出了 Embodied Mobile Manipulation in Open Environments（EMMOE），这一基准要求智能体理解用户指令，并在连续空间中执行长程日常任务。EMMOE将高层与低层具身任务无缝整合到统一框架中，并提供三项新指标以实现更多样化的评估。此外，我们收集了~\dataset，其具有多种任务属性、详细的过程标注、失败后的重新规划，以及两个用于LLM训练的子数据集。进一步地，我们设计了~\model，这是一个精密的智能体系统，由采用直接偏好优化（DPO）的LLM、轻量级导航与操作模型，以及多种错误检测机制组成。最后，我们展示了~\model的性能以及对不同模型和策略的评估。
英文摘要：
Developing autonomous home robots controlled by natural language has long been a pursuit of humanity. 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 propose Embodied Mobile Manipulation in Open Environments (EMMOE), a benchmark that 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~\dataset, which features in various task attributes, detailed process annotations, re-plans after failures, and two sub-datasets for LLM training. Furthermore, we design~\model, 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~\model's performance and evaluations of different models and policies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
