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LEGENT: Open Platform for Embodied Agents

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arxiv 2404.18243 v2 pith:PIC43F4K submitted 2024-04-28 cs.CL cs.AIcs.CVcs.LGcs.RO

classification cs.CLcs.AIcs.CVcs.LGcs.RO
keywords agentsembodiedlegentopendatalargellmslmms
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Despite advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), their integration into language-grounded, human-like embodied agents remains incomplete, hindering complex real-life task performance in physical environments. Existing integrations often feature limited open sourcing, challenging collective progress in this field. We introduce LEGENT, an open, scalable platform for developing embodied agents using LLMs and LMMs. LEGENT offers a dual approach: a rich, interactive 3D environment with communicable and actionable agents, paired with a user-friendly interface, and a sophisticated data generation pipeline utilizing advanced algorithms to exploit supervision from simulated worlds at scale. In our experiments, an embryonic vision-language-action model trained on LEGENT-generated data surpasses GPT-4V in embodied tasks, showcasing promising generalization capabilities.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AI

    cs.AI 2024-12 conditional novelty 6.0 of 10

    A new collection of 100-plus Unreal Engine worlds and tools for embodied AI shows that environment diversity improves tracking agents, while exposing gaps in navigation, cross-embodiment transfer, and latency.

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