**V-IRL: Grounding Virtual Intelligence in Real Life**
The paper introduces V-IRL, an open-source platform designed to bridge the sensory gap between digital and physical worlds, enabling AI agents to interact with the real world in a virtual yet realistic environment. V-IRL leverages real-world geospatial data and street view imagery to provide rich sensory grounding and perception for agents. The platform serves as a playground for developing agents capable of performing various practical tasks and as a testbed for measuring progress in perception, decision-making, and interaction with real-world data.
**Key Contributions:**
1. **V-IRL Platform:** An open-source platform for building and testing agents in a real-world setting, enabling rich sensory grounding and perception.
2. **Diverse Exemplar Agents:** Development of agents that showcase the platform's versatility and adaptability, including Earthbound, Language-Driven, Visually Grounded, and Collaborative agents.
3. **Global Benchmarks:** Creation of benchmarks to evaluate the performance of foundational language and vision models on open-world visual data from diverse geographic and cultural contexts.
**System Fundamentals:**
- **Agent Definition:** Agents are defined by user-defined metadata, including background, intended goal, and interoceptive state.
- **Platform Components:** The platform includes environment, vision, language, and collaboration components, which can be flexibly combined to exhibit a wide range of capabilities.
**Benchmarks:**
- **V-IRL Place Detection:** Evaluates vision models on the task of localizing places using street view imagery.
- **V-IRL Place Recognition and VQA:** Assesses models on recognizing place types and identifying human intentions via Vision Question Answering (VQA).
- **V-IRL Vision-Language Navigation:** Tests the coordination between vision and language models in navigating to destinations using textual directions.
**Discussion:**
- **Ethics and Privacy:** V-IRL addresses ethical and privacy concerns by using preexisting APIs and ensuring compliance with privacy measures.
**Conclusion:**
V-IRL opens new avenues for advancing AI capabilities in perception, decision-making, and real-world data interaction, bridging the gap between virtual agents and visually rich real-world environments.**V-IRL: Grounding Virtual Intelligence in Real Life**
The paper introduces V-IRL, an open-source platform designed to bridge the sensory gap between digital and physical worlds, enabling AI agents to interact with the real world in a virtual yet realistic environment. V-IRL leverages real-world geospatial data and street view imagery to provide rich sensory grounding and perception for agents. The platform serves as a playground for developing agents capable of performing various practical tasks and as a testbed for measuring progress in perception, decision-making, and interaction with real-world data.
**Key Contributions:**
1. **V-IRL Platform:** An open-source platform for building and testing agents in a real-world setting, enabling rich sensory grounding and perception.
2. **Diverse Exemplar Agents:** Development of agents that showcase the platform's versatility and adaptability, including Earthbound, Language-Driven, Visually Grounded, and Collaborative agents.
3. **Global Benchmarks:** Creation of benchmarks to evaluate the performance of foundational language and vision models on open-world visual data from diverse geographic and cultural contexts.
**System Fundamentals:**
- **Agent Definition:** Agents are defined by user-defined metadata, including background, intended goal, and interoceptive state.
- **Platform Components:** The platform includes environment, vision, language, and collaboration components, which can be flexibly combined to exhibit a wide range of capabilities.
**Benchmarks:**
- **V-IRL Place Detection:** Evaluates vision models on the task of localizing places using street view imagery.
- **V-IRL Place Recognition and VQA:** Assesses models on recognizing place types and identifying human intentions via Vision Question Answering (VQA).
- **V-IRL Vision-Language Navigation:** Tests the coordination between vision and language models in navigating to destinations using textual directions.
**Discussion:**
- **Ethics and Privacy:** V-IRL addresses ethical and privacy concerns by using preexisting APIs and ensuring compliance with privacy measures.
**Conclusion:**
V-IRL opens new avenues for advancing AI capabilities in perception, decision-making, and real-world data interaction, bridging the gap between virtual agents and visually rich real-world environments.