  Automating activities through robots in unstructured environments, such as
construction sites, has been a long-standing desire. However, the high degree
of unpredictable events in these settings has resulted in far less adoption
compared to more structured settings, such as manufacturing, where robots can
be hard-coded or trained on narrowly defined datasets. Recently, pretrained
foundation models, such as Large Language Models (LLMs), have demonstrated
superior generalization capabilities by providing zero-shot solutions for
problems do not present in the training data, proposing them as a potential
solution for introducing robots to unstructured environments. To this end, this
study investigates potential opportunities and challenges of pretrained
foundation models from a multi-dimensional perspective. The study
systematically reviews application of foundation models in two field of robotic
and unstructured environment and then synthesized them with deliberative acting
theory. Findings showed that linguistic capabilities of LLMs have been utilized
more than other features for improving perception in human-robot interactions.
On the other hand, findings showed that the use of LLMs demonstrated more
applications in project management and safety in construction, and natural
hazard detection in disaster management. Synthesizing these findings, we
located the current state-of-the-art in this field on a five-level scale of
automation, placing them at conditional automation. This assessment was then
used to envision future scenarios, challenges, and solutions toward autonomous
safe unstructured environments. Our study can be seen as a benchmark to track
our progress toward that future.
