EMMOE: A Comprehensive Benchmark for Embodied Mobile Manipulation in Open Environments

• URL: http://arxiv.org/abs/2503.08604v1
• Date: Tue, 11 Mar 2025 16:42:36 GMT
• Title: EMMOE: A Comprehensive Benchmark for Embodied Mobile Manipulation in Open Environments
• Authors: Dongping Li, Tielong Cai, Tianci Tang, Wenhao Chai, Katherine Rose Driggs-Campbell, Gaoang Wang,
• Abstract summary: 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.<n> EMMOE seamlessly integrates high-level and low-level embodied tasks into a unified framework, along with three new metrics for more diverse assessment.<n>Furthermore, we design HomieBot, a sophisticated agent system consists of LLM with Direct Optimization Preference (DPO), light weighted navigation and manipulation models, and multiple error detection mechanisms.
• Score: 11.97783742296183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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