EMMOE introduces a unified benchmark for evaluating autonomous home robots controlled by natural language, addressing critical gaps in embodied AI evaluation.

• Tackles key challenges in robot task complexity and evaluation metrics
• Bridges the gap between large language models and mobile manipulation trajectories
• Creates standardized assessment methods for embodied AI systems
• Enables more robust testing of robots in open environments

This engineering breakthrough provides developers with a consistent framework to test and improve language-controlled robots for home environments, accelerating progress toward practical autonomous systems that can understand and execute human instructions.

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