Bio-inspired soft robots have gained significant attention for their flexible design and adaptability to various environments, making them suitable for exploration and task execution in confined or hazardous areas. However, the deformation and motion of soft magnetic robots rely on both their structural design and magnetization, which complicates the guided movement and balance maintenance for aquatic environments. In this work, inspired by the flat and symmetrical body of rays, we design a soft magnetic fish-shaped robot capable of flexible motions and trajectory swimming on the water surface. This robot features the muscle made of magnetic elastomer, which connects with the acrylic skeleton and silicone film fins with a soft body. In the external magnetic field, the robot achieves hovering by flapping its fins, driven by the magnetically actuated deformation of its magnetic muscle. Besides, the robot's axial magnetization enables the rapid steering guided by a horizontal field. In experiments, the soft magnetic robot was tasked with performing a looping figure-eight trajectory movement on the water surface, guided by the field gradient generated by a dense planar electromagnetic coils' array. When moving, the onboard circuit board of the robot collected its inertial and temperature information, and sent these data to the host computer via Bluetooth in real-time for motion monitoring. Received data demonstrated that our robot performed the specified afloat swimming trajectory, exhibiting a good stability on its yaw angle during the continuous motion. The soft magnetic swimming robot shows its integrated functionalities in untethered actuation, on-robot sensing, and wireless communication, indicating a significant prospect on applications in inspection and cleaning within narrow pipelines and enclosed mechanical interior spaces.

Manipulating deformable objects is a ubiquitous task in household environments, demanding adequate representation and accurate dynamics prediction due to the objects’ infinite degrees of freedom. This work proposes DeformNet, which utilizes latent space modeling with a learned 3D representation model to tackle these challenges effectively. The proposed representation model combines a PointNet encoder and a conditional neural radiance field (NeRF), facilitating a thorough acquisition of object deformations and variations in lighting conditions. To model the complex dynamics, we employ a recurrent state-space model (RSSM) that accurately predicts the transformation of the latent representation over time. Extensive simulation experiments with diverse objectives demonstrate the generalization capabilities of DeformNet for various deformable object manipulation tasks, even in the presence of previously unseen goals. Finally, we deploy DeformNet on an actual UR5 robotic arm to demonstrate its capability in real-world scenarios.

Surface vibration tactile feedback is capable of conveying various semantic information to humans via handheld electronic devices, such as smartphones, touch panels, and game controllers. However, covering the entire contacting surface of the device with a dense arrangement of actuators can affect its normal use. Determining how to produce desired vibration patterns at any contact point with only a few sparse actuators deployed on the surface of the handheld device remains a significant challenge. In this work, we develop a tactile feedback board in the size of a smartphone with only five actuators, and achieve the precise production of vibration patterns that can focus at any desired position on the board. Specifically, we investigate the vibration characteristics of a single passive coil actuator and construct its vibration pattern model for any position on the feedback board surface. Optimal phase and amplitude modulation, determined using the simulated annealing algorithm, is employed with five actuators in a sparse array. The vibration patterns from all actuators are superimposed linearly to synthetically generate different onboard vibration energy distributions for tactile sensing. Experiments demonstrated that point-wise vibration pattern production on our tactile board achieved an average level of about 0.9 in the Structural Similarity Index Measure (SSIM) evaluation, when compared to the ideal single-point-focused target vibration pattern. Four point-wise patterns focused on the top, bottom, left, and right parts of the tactile board were applied, to guide continuous directional movements without visual assistance, which shows significant implications for machine-assisted cognition based on vibration tactile feedback.