In humans and robots alike, transfer learning occurs at different levels of abstraction, from high-level linguistic transfer to low-level transfer of motor skills. In this article, we provide an overview of the impact that foundation models and transformer networks have had on these different levels, bringing robots closer than ever to "full-stack transfer". Considering LLMs, VLMs and VLAs from a robotic transfer learning perspective allows us to highlight recurring concepts for transfer, beyond specific implementations. We also consider the challenges of data collection and transfer benchmarks for robotics in the age of foundation models. Are foundation models the route to full-stack transfer in robotics? Our expectation is that they will certainly stay on this route as a key technology.
@article{arxiv.2602.22001,
title = {Are Foundation Models the Route to Full-Stack Transfer in Robotics?},
author = {Freek Stulp and Samuel Bustamante and João Silvério and Alin Albu-Schäffer and Jeannette Bohg and Shuran Song},
journal= {arXiv preprint arXiv:2602.22001},
year = {2026}
}