  The rapid development of generative technology opens up possibility for
higher level of automation, and artificial intelligence (AI) embodiment in
robotic systems is imminent. However, due to the blackbox nature of the
generative technology, the generation of the knowledge and workflow scheme is
uncontrolled, especially in a dynamic environment and a complex scene. This
poses challenges to regulations in safety-demanding applications such as
medical scenes. We argue that the unregulated generative processes from AI is
fitted for low level end tasks, but intervention in the form of manual or
automated regulation should happen post-workflow-generation and
pre-robotic-execution. To address this, we propose a roadmap that can lead to
fully automated and regulated robotic systems. In this paradigm, the high level
policies are generated as structured graph data, enabling regulatory oversight
and reusability, while the code base for lower level tasks is generated by
generative models. Our approach aims the transitioning from expert knowledge to
regulated action, akin to the iterative processes of study, practice, scrutiny,
and execution in human tasks. We identify the generative and deterministic
processes in a design cycle, where generative processes serve as a text-based
world simulator and the deterministic processes generate the executable system.
We propose State Machine Seralization Language (SMSL) to be the conversion
point between text simulator and executable workflow control. From there, we
analyze the modules involved based on the current literature, and discuss human
in the loop. As a roadmap, this work identifies the current possible
implementation and future work. This work does not provide an implemented
system but envisions to inspire the researchers working on the direction in the
roadmap. We implement the SMSL and D-SFO paradigm that serve as the starting
point of the roadmap.
