Biomimetic underwater robots have attracted considerable research attention globally, owing to their quieter actuations, higher propulsion efficiency, and stronger maneuverability when compared with conventional underwater vehicles equipped with axial propellers. This article provides a comprehensive survey of current research in this field. First, we review the development status of biomimetic underwater robots in both body/caudal fin (BCF), median/paired fin (MPF), and their hybrid propulsion modes. Then, we outline the motion control methods employed in biomimetic underwater robots, including open-loop swimming control and typical closed-loop control strategies. In particular, we detail our latest studies on the RobCutt series underwater robots. On this basis, some critical issues and future directions are summarized. We predict that biomimetic underwater robots will have excellent prospects in underwater environment exploration and resource utilization.

In-hand object localization and manipulation has always been a challenging task in robotic community. In this article, we address this problem by vision-based tactile sensing with high-spatial resolution. Specifically, we design a novel tactile sensor based on stereo vision, named GelStereo, which can perceive tactile point cloud with high-spatial resolution ( $<\!1$ mm). A tactile-based in-hand object localization pipeline composed of saliency detection and probabilistic point-set registration algorithms of the perceived contact point cloud is presented. Furthermore, extensive qualitative and quantitative analyses of perceived tactile point cloud and in-hand localization and insertion experiments of small parts are performed on our robot platform. The experimental results verify the accuracy and robustness of the tactile point cloud sensed by the novel GelStereo tactile sensor and the proposed in-hand object localization pipeline. This novel high-resolution visuotactile sensing technology has predictable application potential in the field of dexterous robotic manipulation.

Predicting whether a particular grasp will succeed is critical to performing stable grasping and manipulating tasks. Robots need to combine vision and touch as humans do to accomplish this prediction. The primary problem to be solved in this process is how to learn effective visual-tactile fusion features. In this paper, we propose a novel Visual-Tactile Fusion learning method based on Self-Attention mechanism (VTFSA) to address this problem. We compare the proposed method with the traditional methods on two public multimodal grasping datasets, and the experimental results show that the VTFSA model achieves outperforming traditional methods by a margin of 5+% and 7+%. Furthermore, visualization analysis indicate that the VTFSA model can further capture some position-related visual-tactile fusion features that are beneficial to this task and is more robust compared to traditional methods.

In this article, the underwater target tracking control problem of a biomimetic underwater vehicle (BUV) is addressed. Since it is difficult to build an effective mathematic model of a BUV due to the uncertainty of hydrodynamics, target tracking control is converted into the Markov decision process and is further achieved via deep reinforcement learning. The system state and reward function of underwater target tracking control are described. Based on the actor–critic reinforcement learning framework, the deep deterministic policy gradient actor–critic algorithm with supervision controller is proposed. The training tricks, including prioritized experience replay, actor network indirect supervision training, target network updating with different periods, and expansion of exploration space by applying random noise, are presented. Indirect supervision training is designed to address the issues of low stability and slow convergence of reinforcement learning in the continuous state and action space. Comparative simulations are performed to show the effectiveness of the training tricks. Finally, the proposed actor–critic reinforcement learning algorithm with supervision controller is applied to the physical BUV. Swimming pool experiments of underwater object tracking of the BUV are conducted in multiple scenarios to verify the effectiveness and robustness of the proposed method.

Most industrial robots are not capable of teaching by hand and require path points to be specified by teaching pendants. To enable the teaching of industrial robots by hand without any force sensors, this paper proposes a scheme to minimize the external force estimation error and reduce disturbance in the guiding task by using the virtual mass and virtual friction model. In this case, the maximum velocity and acceleration of the robot end-effector shall be limited to ensure safety. Thus, the operator is allowed to guide the robot by hand. The joint torque is obtained from the motor current. The inertial force and friction of the links and driving systems are analyzed. The nonlinear dynamic model of the industrial robot is built and its parameters are calibrated by a nonlinear method. The force estimation is referenced to set the virtual friction and design the force following controller. Hence the end-effector can follow the direction of external force compliantly and suppress jitters. Finally, several experiments on a six-degree-of-freedom industrial robot demonstrate the validity of the proposed control scheme.