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: https://sites.google.com/view/transperception .

Tactile sensing is important for robots to perceive the world as it captures the physical surface properties of the object with which it is in contact and is robust to illumination and colour variances. However, due to the limited sensing area and the resistance of their fixed surface when they are applied with relative motions to the object, current tactile sensors have to tap the tactile sensor on the target object a great number of times when assessing a large surface, i.e., pressing, lifting up, and shifting to another region. This process is ineffective and time-consuming. It is also undesirable to drag such sensors as this often damages the sensitive membrane of the sensor or the object. To address these problems, we propose a roller-based optical tactile sensor named TouchRoller, which can roll around its centre axis. It maintains being in contact with the assessed surface throughout the entire motion, allowing for efficient and continuous measurement. Extensive experiments showed that the TouchRoller sensor can cover a textured surface of 8 cm × 11 cm in a short time of 10 s, much more effectively than a flat optical tactile sensor (in 196 s). The reconstructed map of the texture from the collected tactile images has a high Structural Similarity Index (SSIM) of 0.31 on average when compared with the visual texture. In addition, the contacts on the sensor can be localised with a low localisation error, 2.63 mm in the centre regions and 7.66 mm on average. The proposed sensor will enable the fast assessment of large surfaces with high-resolution tactile sensing and the effective collection of tactile images.

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}.

Recently, tactile sensing has attracted great interest in robotics, especially for facilitating exploration of unstructured environments and effective manipulation. A detailed understanding of the surface textures via tactile sensing is essential for many of these tasks. Previous works on texture recognition using camera based tactile sensors have been limited to treating all regions in one tactile image or all samples in one tactile sequence equally, which includes much irrelevant or redundant information. In this paper, we propose a novel Spatio-Temporal Attention Model (STAM) for tactile texture recognition, which is the very first of its kind to our best knowledge. The proposed STAM pays attention to both spatial focus of each single tactile texture and the temporal correlation of a tactile sequence. In the experiments to discriminate 100 different fabric textures, the spatially and temporally selective attention has resulted in a significant improvement of the recognition accuracy, by up to 18.8%, compared to the non-attention based models. Specifically, after introducing noisy data that is collected before the contact happens, our proposed STAM can learn the salient features efficiently and the accuracy can increase by 15.23% on average compared with the CNN based baseline approach. The improved tactile texture perception can be applied to facilitate robot tasks like grasping and manipulation.