  Transparent object perception is a rapidly developing research problem in
artificial intelligence. The ability to perceive transparent objects enables
robots to achieve higher levels of autonomy, unlocking new applications in
various industries such as healthcare, services and manufacturing. Despite
numerous datasets and perception methods being proposed in recent years, there
is still a lack of in-depth understanding of these methods and the challenges
in this field. To address this gap, this article provides a comprehensive
survey of the platforms and recent advances for robotic perception of
transparent objects. We highlight the main challenges and propose future
directions of various transparent object perception tasks, i.e., segmentation,
reconstruction, and pose estimation. We also discuss the limitations of
existing datasets in diversity and complexity, and the benefits of employing
multi-modal sensors, such as RGB-D cameras, thermal cameras, and polarised
imaging, for transparent object perception. Furthermore, we identify perception
challenges in complex and dynamic environments, as well as for objects with
changeable geometries. Finally, we provide an interactive online platform to
navigate each reference: \url{https://sites.google.com/view/transperception}.
