Industrial robots frequently encounter transparent objects in their work environments. Unlike conventional objects, transparent objects often lack distinct texture features in RGB images and result in incomplete and inaccurate depth images. This presents a significant challenge to robotic perception and operation. As a result, many studies have focused on reconstructing depth data by encoding and decoding RGB and depth information. However, current research faces two limitations: insufficiently addressing challenges posed by textureless transparent objects during the encoding-decoding process and inadequate emphasis on capturing shallow characteristics and cross-modal interaction of RGB-D bimodal data. To overcome these limitations, this study proposes a depth perception network based on orientation-aware guidance and texture enhancement for robots to perceive transparent objects. The backbone network incorporates an orientation-aware guidance module to integrate shallow RGB-D features, providing prior direction. In addition, this study designs a multibranch, multisensory field interactive texture nonlinear enhancement architecture, inspired by human vision, to tackle the challenges presented by textureless transparent objects. The proposed approach is extensively validated on both public datasets and industrial robotics platforms, demonstrating highly competitive performance. 

Robots have always found it challenging to grasp in cluttered scenes because of the complex background information and changing operating environment. Therefore, in order to enable robots to perform multi-object grasping tasks in a wider range of application scenarios, such as object sorting on industrial production lines and object manipulation by home service robots, we innovatively integrated segmentation and grasp detection into the same framework, and designed a simultaneous instance segmentation and grasp detection network (SISG-Net). By using the network, robots can better interact with complex environments and perform grasp tasks. In order to solve the problem of insufficient fusion of the existing RGBD fusion strategy in the robot field, we propose a lightweight RGB-D fusion module called SMCF to make modal fusion more efficient. In order to solve the problem of inaccurate perception of small objects in different scenes, we propose the FFASP module. Finally, we use the AFF module to adaptively fuse multi-scale features. Segmentation can remove noise information from the background, enabling the robot to grasp in different backgrounds with robustness. Using the segmentation result, we refine grasp detection and find the best grasp pose for robot grasping in complex scenes. Our grasp detection model performs similarly to state-of-the-art grasp detection algorithms on the Cornell Dataset. Our model achieves state-of-the-art performance on the OCID Dataset. We show that the method is stable and robust in real-world grasping experiments. The code and video of our experiment used in this paper can be found at: 

https://github.com/meiguiz/SISG-Net

It is challenging for robots to detect grasps with high accuracy and efficiency-oriented to multi-object clutter scenes, especially scenes with objects of large-scale differences. Effective grasping representation, full utilization of data, and formulation of grasping strategies are critical to solving the problem. To this end, this paper proposes an antipodal-points grasping representation model. Based on this, the Antipodal-Points-aware Dual-decoding Network (APDNet) is presented for grasping detection in multi-object scenes. APDNet employs an encoding–decoding architecture. The shared encoding strategy based on an Adaptive Gated Fusion Module (AGFM) is proposed in the encoder to fuse RGB-D multimodal data. Two decoding branches, namely StartpointNet and EndpointNet, are presented to detect antipodal points. To better focus on objects at different scales in multi-object scenes, a global multi-view cumulative attention mechanism, called Global Accumulative Attention Mechanism (GAAM), is also designed in this paper for StartpointNet. The proposed method is comprehensively validated and compared using a public dataset and real robot platform. On the GraspNet-1Billion dataset, the proposed method achieves 30.7%, 26.4%, and 12.7% accuracy at a speed of 88.4 FPS for seen, unseen, and novel objects, respectively. On the AUBO robot platform, the detection and grasp success rates are 100.0% and 95.0% on single-object scenes and 97.0% and 90.3% on multi-object scenes, respectively. It is demonstrated that the proposed method exhibits state-of-the-art performance with well-balanced accuracy and efficiency.