"EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents"

The paper proposes a new benchmark, EmbodiedEval, to evaluate Multimodal LLMs in embodied agents, addressing the gap in current benchmarks that don't fully assess complex multimodal understanding and interaction needed for embodied AI.

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Paper - https://arxiv.org/abs/2501.11858

Original Problem 🧐:

→ Current benchmarks for Multimodal LLMs are limited in evaluating complex multimodal understanding.

→ They do not adequately assess the capabilities of Multimodal LLMs in embodied agent scenarios.

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Solution in this Paper 💡:

→ This paper introduces EmbodiedEval, a new benchmark to evaluate Multimodal LLMs as embodied agents.

→ EmbodiedEval includes five diverse tasks: Attribute Question Answering, Spatial Question Answering, Navigation, Object Interaction, and Social Interaction.

→ These tasks are designed to comprehensively assess different facets of embodied intelligence.

→ These facets include perception, reasoning, and action within embodied environments.

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Key Insights from this Paper 🧠:

→ Existing Multimodal LLMs, while advanced, still underperform humans in embodied tasks.

→ Embodied AI tasks require more sophisticated multimodal understanding than current benchmarks assess.

→ EmbodiedEval highlights critical areas for future research in Multimodal LLMs for embodied agents.

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Results 📊:

→ The paper evaluates several state-of-the-art Multimodal LLMs on EmbodiedEval.

→ The results demonstrate a performance gap between current Multimodal LLMs and human-level embodied intelligence.