Abstract
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.
Abstract (translated)
在人类和机器人中，迁移学习发生在不同层次的抽象层面，从高层次的语言转换到低层次的动作技能转移。本文概述了基础模型和变压器网络对这些不同层次的影响，使机器人比以往任何时候都更接近“全栈迁移”。从机器人迁移学习的角度考虑大语言模型（LLMs）、视觉语言模型（VLMs）以及视觉语言助手（VLAs），可以帮助我们突出超越特定实现的迁移概念。我们还探讨了在基础模型时代收集数据和建立转移基准给机器人技术带来的挑战。基础模型是否是通往机器人全栈迁移的道路？我们的预期是，它们无疑将继续作为关键技术沿着这一路径发展。
URL
https://arxiv.org/abs/2602.22001
