LEGENT: Open Platform for Embodied Agents
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Abstract (arXiv)
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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Placed in World models for navigation, one of 11 directions in World models. Builds on 48 of its 57 references that are in the map. First posted on arXiv 28 Apr 2024.
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- 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,168
35 more
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- 2018Gibson Env: Real-World Perception for Embodied Agentscited by 477
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- 2018VirtualHome: Simulating Household Activities via Programscited by 411
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- 2021iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Taskscited by 191
- 2017IQA: Visual Question Answering in Interactive Environmentscited by 154
- 2020Rearrangement: A Challenge for Embodied AIcited by 153
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- 2022A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learningcited by 60
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Built on by
Citing papers per year: 2024: 4; 2025: 4; 2026: 4.
12 papers.
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- 2024MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agentscited by 11
- 2025EscapeCraft: A 3D Room Escape Environment for Benchmarking Complex Multimodal Reasoning Abilitycited by 6
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- 2026Do World Models Make Better Robots? A Survey of Evaluation Benchmarks for Predictive Embodied Intelligence30 Aug 2026
- 2026UniETP: Unifying Environments for Generalizable Embodied Task Planning20 Jul 2026
- 2026SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning10 May 2026
- 2025Virtual Community: An Open World for Humans, Robots, and Society20 Aug 2025
- 2025Static and Plugged: Make Embodied Evaluation Simple6 Aug 2025
- 2025EscapeCraft: A 3D Room Escape Environment for Benchmarking Complex Multimodal Reasoning Ability13 Mar 2025
- 2025EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents21 Jan 2025
- 2024UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AI30 Dec 2024
- 2024Understanding World or Predicting Future? A Comprehensive Survey of World Models21 Nov 2024
- 2024MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agents4 Oct 2024
- 2024Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI9 Jul 2024
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