Embodied Web Agents: Bridging Physical-Digital Realms for Integrated
  Agent Intelligence
Authors: name: Yining Hong, name: Rui Sun, name: Bingxuan Li, name: Xingcheng Yao, name: Maxine Wu, name: Alexander Chien, name: Da Yin, name: Ying Nian Wu, name: Zhecan James Wang, name: Kai-Wei Chang

AI agents today are mostly siloed - they either retrieve and reason over vast
amount of digital information and knowledge obtained online; or interact with
the physical world through embodied perception, planning and action - but
rarely both. This separation limits their ability to solve tasks that require
integrated physical and digital intelligence, such as cooking from online
recipes, navigating with dynamic map data, or interpreting real-world landmarks
using web knowledge. We introduce Embodied Web Agents, a novel paradigm for AI
agents that fluidly bridge embodiment and web-scale reasoning. To
operationalize this concept, we first develop the Embodied Web Agents task
environments, a unified simulation platform that tightly integrates realistic
3D indoor and outdoor environments with functional web interfaces. Building
upon this platform, we construct and release the Embodied Web Agents Benchmark,
which encompasses a diverse suite of tasks including cooking, navigation,
shopping, tourism, and geolocation - all requiring coordinated reasoning across
physical and digital realms for systematic assessment of cross-domain
intelligence. Experimental results reveal significant performance gaps between
state-of-the-art AI systems and human capabilities, establishing both
challenges and opportunities at the intersection of embodied cognition and
web-scale knowledge access. All datasets, codes and websites are publicly
available at our project page https://embodied-web-agent.github.io/.