Digital twins to embodied artificial intelligence: review and perspective

Abstract

Embodied artificial intelligence (AI) is reshaping the landscape of intelligent robotic systems, particularly by providing many realistic solutions to execute actions in complex and dynamic environments. However, Embodied AI requires a huge data generation for training and evaluation to ensure safe interaction with physical environments. Therefore, it is necessary to build a cost-effective simulated environment that can provide enough data for training and optimization from the physical characteristics, object properties, and interactions. Digital twins (DTs) are vital issues in Industry 5.0, which enable real-time monitoring, simulation, and optimization of physical processes by mirroring the state and action of their real-world counterparts. This review explores how integrating DTs with Embodied AI can bridge the sim-to-real gap by transforming virtual environments into dynamic and data-rich platforms. The integration of DTs offers real-time monitoring and virtual simulations, enabling Embodied AI agents to train and adapt in virtual environments before deployment in real-world scenarios. In this review, the main challenges and the novel perspective of the future development of integrating DTs and Embodied AI are discussed. To the best of our knowledge, this is the first work to comprehensively review the synergies between DTs and Embodied AI.

Keywords

Digital twins / Embodied AI / robotic digital twins / human digital twins / human-robot collaboration