AIJan 12

VirtualEnv: A Platform for Embodied AI Research

arXiv:2601.07553v12 citationsh-index: 2Has Code
Originality Incremental advance
AI Analysis

This provides a standardized platform for researchers to evaluate LLMs in interactive scenarios, advancing embodied AI and gaming research, though it is incremental as it builds on existing simulation and LLM technologies.

The paper tackles the need for realistic environments to evaluate large language models (LLMs) in embodied AI by introducing VirtualEnv, a simulation platform built on Unreal Engine 5 that enables fine-grained benchmarking, and it benchmarks several LLMs across complex tasks, analyzing adaptability and coordination.

As large language models (LLMs) continue to improve in reasoning and decision-making, there is a growing need for realistic and interactive environments where their abilities can be rigorously evaluated. We present VirtualEnv, a next-generation simulation platform built on Unreal Engine 5 that enables fine-grained benchmarking of LLMs in embodied and interactive scenarios. VirtualEnv supports rich agent-environment interactions, including object manipulation, navigation, and adaptive multi-agent collaboration, as well as game-inspired mechanics like escape rooms and procedurally generated environments. We provide a user-friendly API built on top of Unreal Engine, allowing researchers to deploy and control LLM-driven agents using natural language instructions. We integrate large-scale LLMs and vision-language models (VLMs), such as GPT-based models, to generate novel environments and structured tasks from multimodal inputs. Our experiments benchmark the performance of several popular LLMs across tasks of increasing complexity, analyzing differences in adaptability, planning, and multi-agent coordination. We also describe our methodology for procedural task generation, task validation, and real-time environment control. VirtualEnv is released as an open-source platform, we aim to advance research at the intersection of AI and gaming, enable standardized evaluation of LLMs in embodied AI settings, and pave the way for future developments in immersive simulations and interactive entertainment.

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