  Language-conditioned robotic manipulation represents a cutting-edge area of
research, enabling seamless communication and cooperation between humans and
robotic agents. This field focuses on teaching robotic systems to comprehend
and execute instructions conveyed in natural language. To achieve this, the
development of robust language understanding models capable of extracting
actionable insights from textual input is essential. In this comprehensive
survey, we systematically explore recent advancements in language-conditioned
approaches within the context of robotic manipulation. We analyze these
approaches based on their learning paradigms, which encompass reinforcement
learning, imitation learning, and the integration of foundational models, such
as large language models and vision-language models. Furthermore, we conduct an
in-depth comparative analysis, considering aspects like semantic information
extraction, environment & evaluation, auxiliary tasks, and task representation.
Finally, we outline potential future research directions in the realm of
language-conditioned learning for robotic manipulation, with the topic of
generalization capabilities and safety issues. The GitHub repository of this
paper can be found at
https://github.com/hk-zh/language-conditioned-robot-manipulation-models
