Foundation Models in Robotics: A Comprehensive Review of Methods, Models, Datasets, Challenges and Future Research Directions

A consortium of seven researchers from academic institutions has published a landmark review paper, 'Foundation Models in Robotics: A Comprehensive Review of Methods, Models, Datasets, Challenges and Future Research Directions,' on arXiv. The study, submitted in April 2026, represents a systematic effort to map the transformative impact of large-scale AI models—like large language models (LLMs), vision-language models (VLMs), and vision-language-action models (VLAs)—on the field of robotics. The authors argue these models are driving a fundamental shift from domain-specific, single-task robots toward adaptive, multi-function agents capable of operating in complex, open-world environments.

The paper structures its analysis across five distinct research phases, tracing the evolution from early integrations of NLP and computer vision models to the current frontier of multi-sensory generalization and real-world deployment. It provides a highly granular taxonomic investigation, examining key aspects including the types of foundation models employed, underlying neural architectures, learning paradigms, stages of knowledge incorporation, major robotic tasks, and primary application domains. For each category, the authors offer comparative analysis and critical insights.

Furthermore, the review catalogs publicly available datasets used for training and evaluating models across various robotic tasks. It concludes with a hierarchical discussion of current open challenges—such as ensuring safety, reliability, and efficient real-world deployment—and outlines promising future research directions. This comprehensive document, spanning over 7,200 KB, is positioned to serve as an essential reference for researchers and engineers navigating the rapidly converging fields of AI and robotics.