# V-IRL: Grounding Virtual Intelligence in Real Life

arXiv: https://arxiv.org/abs/2402.03310 · HTML: https://mlsanity.com/paper/2402.03310/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 73 of its 92 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 30 of 73)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)

## Built on by (most cited first, 30 of 35)

- [SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training](https://mlsanity.com/paper/2501.17161/) (2025)
- [Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces](https://mlsanity.com/paper/2412.14171/) (2024)
- [SpatialBot: Precise Spatial Understanding with Vision Language Models](https://mlsanity.com/paper/2406.13642/) (2024)
- [The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies](https://mlsanity.com/paper/2407.19354/) (2024)
- [Benchmarking Mobile Device Control Agents across Diverse Configurations](https://mlsanity.com/paper/2404.16660/) (2024)
- [CityNav: A Large-Scale Dataset for Real-World Aerial Navigation](https://mlsanity.com/paper/2406.14240/) (2024)
- [EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environment](https://mlsanity.com/paper/2410.09604/) (2024)
- [Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models](https://mlsanity.com/paper/2407.05342/) (2024)
- [CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks](https://mlsanity.com/paper/2406.13945/) (2024)
- [When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments](https://mlsanity.com/paper/2407.18957/) (2024)
- [UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AI](https://mlsanity.com/paper/2412.20977/) (2024)
- [UrbanWorld: An Urban World Model for 3D City Generation](https://mlsanity.com/paper/2407.11965/) (2024)
- [Virtual Community: An Open World for Humans, Robots, and Society](https://mlsanity.com/paper/2508.14893/) (2025)
- [Can 3D Vision-Language Models Truly Understand Natural Language?](https://mlsanity.com/paper/2403.14760/) (2024)
- [ActiView: Evaluating Active Perception Ability for Multimodal Large Language Models](https://mlsanity.com/paper/2410.04659/) (2024)
- [UrbanFeel: A Comprehensive Benchmark for Temporal and Perceptual Understanding of City Scenes through Human Perspective](https://mlsanity.com/paper/2509.22228/) (2025)
- [Out of Sight, Not Out of Context? Egocentric Spatial Reasoning in VLMs Across Disjoint Frames](https://mlsanity.com/paper/2505.24257/) (2025)
- [TravelAgent: Generative Agents in the Built Environment](https://mlsanity.com/paper/2412.18985/) (2024)
- [Cambrian-P: Pose-Grounded Video Understanding](https://mlsanity.com/paper/2605.22819/) (2026)
- [H2VU-Benchmark: A Comprehensive Benchmark for Hierarchical Holistic Video Understanding](https://mlsanity.com/paper/2503.24008/) (2025)
- [Beyond Language: Grounding Referring Expressions with Hand Pointing in Egocentric Vision](https://mlsanity.com/paper/2603.26646/) (2026)
- [Visually-grounded Humanoid Agents](https://mlsanity.com/paper/2604.08509/) (2026)
- [DeliveryGym: An RL Environment for Long-Horizon Embodied Agent Planning with Adaptive Curriculum](https://mlsanity.com/paper/2609.19801/) (2026)
- [VC-Agent: An Interactive Agent for Customized Video Dataset Collection](https://mlsanity.com/paper/2509.21291/) (2025)
- [CartoMapQA: A Fundamental Benchmark Dataset Evaluating Vision-Language Models on Cartographic Map Understanding](https://mlsanity.com/paper/2512.03558/) (2025)
- [Less Detail, Better Answers: Degradation-Driven Prompting for VQA](https://mlsanity.com/paper/2604.04838/) (2026)
- [From Where Things Are to What They Are For: Benchmarking Spatial-Functional Intelligence in Multimodal LLMs](https://mlsanity.com/paper/2605.02130/) (2026)
- [Do VLMs See What Sensors Feel? A Scalable Expert-Guided Design for Wheelchair Accessibility Assessment from Street View](https://mlsanity.com/paper/2606.07642/) (2026)
- [Hybrid Advantage Estimation with Unified Critic for VLM Agentic Reinforcement Learning](https://mlsanity.com/paper/2607.23605/) (2026)
- [360CityArena: A Realistic Virtual Urban Navigation Benchmark for Embodied Agents](https://mlsanity.com/paper/2608.08814/) (2026)

## Abstract (arXiv)

There is a sensory gulf between the Earth that humans inhabit and the digital realms in which modern AI agents are created. To develop AI agents that can sense, think, and act as flexibly as humans in real-world settings, it is imperative to bridge the realism gap between the digital and physical worlds. How can we embody agents in an environment as rich and diverse as the one we inhabit, without the constraints imposed by real hardware and control? Towards this end, we introduce V-IRL: a platform that enables agents to scalably interact with the real world in a virtual yet realistic environment. Our platform serves as a playground for developing agents that can accomplish various practical tasks and as a vast testbed for measuring progress in capabilities spanning perception, decision-making, and interaction with real-world data across the entire globe.