  Large Language Models (LLMs) and strong vision models have enabled rapid
research and development in the field of Vision-Language-Action models that
enable robotic control. The main objective of these methods is to develop a
generalist policy that can control robots with various embodiments. However, in
industrial robotic applications such as automated assembly and disassembly,
some tasks, such as insertion, demand greater accuracy and involve intricate
factors like contact engagement, friction handling, and refined motor skills.
Implementing these skills using a generalist policy is challenging because
these policies might integrate further sensory data, including force or torque
measurements, for enhanced precision. In our method, we present a global
control policy based on LLMs that can transfer the control policy to a finite
set of skills that are specifically trained to perform high-precision tasks
through dynamic context switching. The integration of LLMs into this framework
underscores their significance in not only interpreting and processing language
inputs but also in enriching the control mechanisms for diverse and intricate
robotic operations.
