V-IRL: Grounding Virtual Intelligence in Real Life
Jihan Yang, Runyu Ding, Ellis Brown, Xiaojuan Qi, Saining Xie

TL;DR
V-IRL is a platform that bridges the gap between digital AI environments and real-world physical settings, enabling scalable, realistic interactions for developing and testing versatile AI agents.
Contribution
It introduces V-IRL, a novel platform that allows virtual agents to interact with real-world data and environments, facilitating progress in perception, decision-making, and physical interaction capabilities.
Findings
Enables scalable real-world interaction in virtual environments
Supports development of agents for diverse practical tasks
Provides a comprehensive testbed for AI capability measurement
Abstract
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.
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Taxonomy
TopicsSemantic Web and Ontologies · Robotics and Automated Systems
