Current industrial robotic manipulators have made evident their lack of flexibility. The systems must know beforehand the piece and it’s position. To address this issue, contemporary approaches typically employ learning-based techniques, which rely on extensive amounts of data. To obtain vast data, an often sought tool is an extensive grasp dataset. This work introduces our Physics-Based Self-Supervised Grasp Pose Detection (PBSS-GPD) pipeline for model-based grasping point detection, useful to generate grasp pose datasets. Given a gripper-object pair, it samples grasping pose candidates using a modified version of GPD (implementing inner-grasps, CAD support...) and quantifies their quality using the MuJoCo physics engine and a grasp quality metric that takes into account the pose of the object over time. The system is optimised to run on CPU in headless-parallelised mode, with the option of running in a graphical interface or headless and storing videos of the process. The system has been validated obtaining grasping poses for a subset of Egad! objects using the Franka Panda two-finger gripper, compared with state of the art grasp generation pipelines and tested in a real scenario. While our system achieves similar accuracy compared to a contemporary approach, 84% on the real world validation, it has proven to be effective at generating grasps with good centering 18 times faster than the compared system.

Although the behavioral mechanism studying is a difficult and complex task, it can produce an important systems optimization impact. In this work, we use, in this paper, inference system approach to represent reasoning mechanism and the operations of dynamic systems to extract mental representations from traveling salesman and convert them into cognitive structures. For this we develop an extraction automatic method to create knowledge bases and, later, data are stocked into structure based on transition maps and the performances of these created maps get improved through combining the reinforcement learning thus augment traveller's deciding capacity from historical data. These transition maps help to find best actions for obtaining useful new policies. Generated intermediate transition maps are gathered to give a global map called main map whose advantage is to improve the learning process. The main idea of this approach is to improve learning capability by using a reinforcement learning technique as exploration and exploitation strategy of the metaheuristique GRASP method and the use of the fuzze logical rules mechanism allowing concepts model to have more variability of states. The results obtained after simulation as presented in this paper are very encouraging.

We introduce a novel hybrid Transformer-CNN architecture for robotic grasp detection, designed to enhance the accuracy of grasping unknown objects. Our proposed architecture has two key designs. Firstly, we develop a hierarchical transformer as the encoder, incorporating the external attention to effectively capture the correlation features across the data. Secondly, the decoder is constructed with cross-layer connections to efficiently fuse multi-scale features. Channel attention is introduced in the decoder to model the correlation between channels and to adaptively recalibrate the channel correlation feature response, thereby increasing the weight of the effective channels. Our method is evaluated on the Cornell and Jacquard public datasets, achieving an image-wise detection accuracy of 98.3% and 95.8% on each dataset, respectively. Additionally, we achieve object-wise detection accuracy of 96.9% and 92.4% on the same datasets. A physical experiment is also performed using the Elite 6Dof robot, with a grasping accuracy rate of 93.3%, demonstrating the proposed method's ability to grasp unknown objects in real-world scenarios. The results of this study show that our proposed method outperforms other state-of-the-art methods.

The connected vertex cover (CVC) problem is a variant of the vertex cover problem, which has many important applications, such as wireless network design, routing and wavelength assignment problem, etc. A good algorithm for the problem can help us improve engineering efficiency, cost savings and resources in industrial applications. In this work, we present an efficient algorithm GRASP-CVC (Greedy Randomized Adaptive Search Procedure for Connected Vertex Cover) for CVC in general graphs. The algorithm has two main phases, i.e., construction phase and local search phase. To construct a high quality feasible initial solution, we design a greedy function and a restricted candidate list in the construction phase. The configuration checking strategy is adopted to decrease the cycling problem in the local search phase. The experimental results demonstrate that GRASP-CVC is competitive with the other competitive algorithm, which validate the effectivity and efficiency of our GRASP-CVC solver.

In the field of vision-based robot grasping, effectively leveraging RGB and depth information to accurately determine the position and pose of a target is a critical issue. To address this challenge, we propose a tri-stream cross-modal fusion architecture for 2-DoF visual grasp detection. This architecture facilitates the interaction of RGB and depth bilateral information and is designed to efficiently aggregate multiscale information. Our novel modal interaction module (MIM) with spatial-wise cross-attention algorithm adaptively captures cross-modal feature information. Meanwhile, the channel interaction modules (CIM) further enhance the aggregation of different modal streams. In addition, we efficiently aggregate global multiscale information through a hierarchical structure with skipping connections. To evaluate the performance of our proposed method, we conduct validation experiments on standard public datasets and real robot grasping experiments. We achieve the image-wise detection accuracy of 99.4% and 96.7% on Cornell and Jacquard datasets respectively. The object-wise detection accuracy reaches 97.8% and 94.6% on the same datasets. Furthermore, physical experiments using the 6-DoF Elite robot demonstrate a success rate of 94.5%. These experiments highlight the superior accuracy of our proposed method.

Robot grasp modeling and implementation is one of essential abilities for a robot with embodied artificial intelligence. However, most existing deep learning-based grasp methods suffer a large number of parameters and heavy computation overheads. To address this issue, by fully exploiting the complementary capabilities of both CNNs and Transformers, we propose a smart grasp deep network with one-way fusion strategy via context path and spatial path (SGNet), which enjoys a lightweight structure, fast inference speed and easy deployment on devices with limited computation resources. Specifically, the context path employs lightweight depthwise separable convolution to achieve fast down-sampling while a novel DSFormer module mainly by integrating Transformer is to extract global and context-rich features. The spatial path efficiently fuses feature information from the context path in one-way manner and generate high-resolution feature maps via point-by-point convolution operations. Experimental results show the proposed model with only 1M parameters has a significantly overall performance, achieving 99.4% accuracy on Cornell dataset and 93.4% accuracy on Jacquard dataset, as well as within 12.5ms inference time.

In order for robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities in order to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged on top of an object to securely grab it between the robotic gripper and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods have driven robotics researchers to explore the application of deep learning methods in task generalised robotic applications. This paper reviews the current state-of-the-art in regards to the application of deep learning methods to generalised robotic grasping and discusses how each element of the deep learning approach has improved the overall performance of robotic grasp detection. A number of the most promising approaches are evaluated and the most successful for grasp detection is identified as the one-shot detection method. The availability of suitable volumes of appropriate training data is identified as a major obstacle for effective utilisation of the deep learning approaches, and the use of transfer learning techniques is identified as a potential mechanism to address this. Finally, current trends in the field and future potential research directions are discussed.