In this paper, we propose a design of an underwater soft snake-like robot prototype that uses two actuators made of 3D-printed soft materials to build the robot body. Control signals with appropriate displacement phases and different voltages are used to control the water pump to drive the soft actuator to bend to generate a sine wave with increasing amplitude along the body axis. We test customized tail materials, phase shifts, and voltage growth rate signals to observe the effects of different parameters on the movement of the snake robot in water. Experiments show that the movement speed is positively correlated with the swing amplitude of the snake robot's motion module. In addition, measured data show that swimming efficiency and movement speed are also affected by tail flexibility and movement gait. When the phase offset is 2/3π, the tail is made of harder PLA material, and the voltage growth rate is 1.2, the maximum underwater movement speed achieved by the snake robot is 4.464 cm/s (0.076 BL/s). We also found that when the phase offset increases, the snake motion speed and motion efficiency first increase and then decrease. The results obtained in this study will aid in the advancement of soft, slender swimming robots and improve the understanding of the swimming capabilities of both robots and sea snakes.

Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes. However, the inherent suboptimal nature of node connections, resulting from the complex and contingent formation process of graphs, presents significant challenges in modeling them effectively. To tackle this issue, Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models. Despite the proposal of numerous GSL methods, the progress in this field remains unclear due to inconsistent experimental protocols, including variations in datasets, data processing techniques, and splitting strategies. In this paper, we introduce OpenGSL, the first comprehensive benchmark for GSL, aimed at addressing this gap. OpenGSL enables a fair comparison among state-of-the-art GSL methods by evaluating them across various popular datasets using uniform data processing and splitting strategies. Through extensive experiments, we observe that existing GSL methods do not consistently outperform vanilla GNN counterparts. We also find that there is no significant correlation between the homophily of the learned structure and task performance, challenging the common belief. Moreover, we observe that the learned graph structure demonstrates a strong generalization ability across different GNN models, despite the high computational and space consumption. We hope that our open-sourced library will facilitate rapid and equitable evaluation and inspire further innovative research in this field. The code of the benchmark can be found in https://github.com/OpenGSL/OpenGSL.

In this paper, a bilateral teleoperation control of a serial robot manipulator, which guarantees a Remote Center of Motion (RCM) constraint in its kinematic level, is developed. A two-layered approach based on the energy tank model is proposed to achieve haptic feedback on the end effector with a pedal switch. The redundancy of the manipulator is exploited to maintain the RCM constraint using the decoupled Cartesian Admittance Control. Transparency and stability of the proposed bilateral teleoperation are demonstrated using a KUKA LWR4+ serial robot and a Sigma 7 haptic manipulator with an RCM constraint in augmented reality. The results prove that the control can achieve not only the bilateral teleoperation but also maintain the RCM constraint.

Teaching by demonstration (TbD) is a powerful way to generalize the skills learned from human demonstrations to fulfill complex requirements. The mobile robot can learn new skills from interaction with human being in an intuitive way. In this paper, we propose a human-robot skill transfer system for a mobile robot that is instructed to follow a trajectory demonstrated by a human teacher wearing a motion capturing device while the Kinect sensor is recording the trajectory. With multi-modal sensor fusion, the position and velocity of the human teacher are enhanced for the correction and accuracy. A nonlinear system named a dynamic movement primitive (DMP) is modeled by the trajectories data. The Gaussian mixture model is applied for the appraisal of DMP, so as to model numerous trajectories through the teaching of a demonstration. Further, to achieve the accuracy of the trajectory tracking, a novel nonlinear model predictive control (MPC) approach is proposed for motion control. By comparison with other obstacle avoidance works, the results show improved performances in terms of the computing time and length of the trajectory.