EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents
This addresses the problem of inadequate evaluation for embodied AI capabilities in MLLMs, which is crucial for researchers and developers in AI and robotics, though it is incremental as it builds on existing embodied AI benchmarks.
The authors tackled the lack of comprehensive benchmarks for evaluating multimodal large language models (MLLMs) as embodied agents by proposing EmbodiedEval, a benchmark with 328 tasks across 125 3D scenes, and found that state-of-the-art MLLMs significantly underperform compared to humans on these tasks.
Multimodal Large Language Models (MLLMs) have shown significant advancements, providing a promising future for embodied agents. Existing benchmarks for evaluating MLLMs primarily utilize static images or videos, limiting assessments to non-interactive scenarios. Meanwhile, existing embodied AI benchmarks are task-specific and not diverse enough, which do not adequately evaluate the embodied capabilities of MLLMs. To address this, we propose EmbodiedEval, a comprehensive and interactive evaluation benchmark for MLLMs with embodied tasks. EmbodiedEval features 328 distinct tasks within 125 varied 3D scenes, each of which is rigorously selected and annotated. It covers a broad spectrum of existing embodied AI tasks with significantly enhanced diversity, all within a unified simulation and evaluation framework tailored for MLLMs. The tasks are organized into five categories: navigation, object interaction, social interaction, attribute question answering, and spatial question answering to assess different capabilities of the agents. We evaluated the state-of-the-art MLLMs on EmbodiedEval and found that they have a significant shortfall compared to human level on embodied tasks. Our analysis demonstrates the limitations of existing MLLMs in embodied capabilities, providing insights for their future development. We open-source all evaluation data and simulation framework at https://github.com/thunlp/EmbodiedEval.