Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research direction. It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering, recommendation systems, and etc. According to the graph types, existing KGR models can be roughly divided into three categories, i.e., static models, temporal models, and multi-modal models. Early works in this domain mainly focus on static KGR, and recent works try to leverage the temporal and multi-modal information, which are more practical and closer to real-world. However, no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a first survey for knowledge graph reasoning tracing from static to temporal and then to multi-modal KGs. Concretely, the models are reviewed based on bi-level taxonomy, i.e., top-level (graph types) and base-level (techniques and scenarios). Besides, the performances, as well as datasets, are summarized and presented. Moreover, we point out the challenges and potential opportunities to enlighten the readers.

A vision-based tactile sensor (VBTS) is an innovative optical sensor widely applied in robotic perception. The VBTS consists of a contact module, an illumination module, and a camera module. Its hardware technologies contain preparation, performance, functions, materials, and optimization design of components. However, the current literature lacks a review of hardware technologies to formulate a complete manufacturing scheme. Therefore, this article focuses on hardware technologies and reviewed the literature over the past five years. We analyze the core components of each module and sort out a technical route. The current challenges and problems of hardware technologies are discussed to propose some feasible solutions. Considering cross-disciplinary applications, we think that multidisciplinary hardware technologies are expected to promote the development of next-generation VBTSs. In addition, we aim to provide a valuable guideline for developers to improve the performance of VBTSs.

We present RobotGPT, an innovative decision framework for robotic manipulation that prioritizes stability and safety. The execution code generated by ChatGPT cannot guarantee the stability and safety of the system. ChatGPT may provide different answers for the same task, leading to unpredictability. This instability prevents the direct integration of ChatGPT into the robot manipulation loop. Although setting the temperature to 0 can generate more consistent outputs, it may cause ChatGPT to lose diversity and creativity. Our objective is to leverage ChatGPT's problem-solving capabilities in robot manipulation and train a reliable agent. The framework includes an effective prompt structure and a robust learning model. Additionally, we introduce a metric for measuring task difficulty to evaluate ChatGPT's performance in robot manipulation. Furthermore, we evaluate RobotGPT in both simulation and real-world environments. Compared to directly using ChatGPT to generate code, our framework significantly improves task success rates, with an average increase from 38.5% to 91.5%. Therefore, training a RobotGPT by utilizing ChatGPT as an expert is a more stable approach compared to directly using ChatGPT as a task planner.

With the advance of smart material science, robotics is evolving from rigid robots to soft robots. Compared to rigid robots, soft robots can safely interact with the environment, easily navigate in unstructured fields, and be minimized to operate in narrow spaces, owning to the new actuation and sensing technologies developed by the smart materials. In the review, different actuation and sensing technologies based on different smart materials are analyzed and summarized. According to the driving or feedback signals, actuators are categorized into electrically responsive actuators, thermally responsive actuators, magnetically responsive actuators, and photoresponsive actuators; sensors are categorized into resistive sensors, capacitive sensors, magnetic sensors, and optical waveguide sensors. After introducing the principle and several robotic prototypes of some typical materials in each category of the actuators and sensors. The advantages and disadvantages of the actuators and sensors are compared based on the categories, and their potential applications in robotics are also presented.

Robotic grasping has become increasingly important in many application areas such as industrial manufacturing and logistics. Because of the diversity and uncertainty of objects and environments, common grippers with one single grasping mode face difficulties to fulfill all the tasks. Hence, we proposed a soft gripper with multiple grasping modes in this study. The gripper consists of four modular soft fingers integrated with layer jamming structure and tendon-driven mechanism. Each finger's rotating shaft of the base uses a torsional spring to decouple the bending deformation and relative rotation. An octopus-mimicking vacuum sucker is installed in the fingertip to generate suction. The effectiveness of the bending deformation and variable stiffness of the design were proved by finite element simulation. Thus, the control model of the finger was built, and the control strategy of multimode grasping of the gripper was proposed. Three control modes were designed to realize the four anthropomorphic grasping modes, including wrap, pinch, hook, and suck. Furthermore, the grasping performance was evaluated to show the abilities. The experiments indicated the superior performance of the proposed gripper and the multimode grasping ability that satisfies various grasping tasks.

Myoelectric control for prosthetic hands is an important topic in the field of rehabilitation. Intuitive and intelligent myoelectric control can help amputees to regain upper limb function. However, current research efforts are primarily focused on developing rich myoelectric classifiers and biomimetic control methods, limiting prosthetic hand manipulation to simple grasping and releasing tasks, while rarely exploring complex daily tasks. In this article, we conduct a systematic review of recent achievements in two areas, namely, intention recognition research and control strategy research. Specifically, we focus on advanced methods for motion intention types, discrete motion classification, continuous motion estimation, unidirectional control, feedback control, and shared control. In addition, based on the above review, we analyze the challenges and opportunities for research directions of functionality-augmented prosthetic hands and user burden reduction, which can help overcome the limitations of current myoelectric control research and provide development prospects for future research.