Untethered magnetic manipulation of biomedical millirobots has a high potential for minimally invasive surgical applications. However, it is still challenging to exert high actuation forces on the small robots over a large distance. Permanent magnets offer stronger magnetic torques and forces than electromagnetic coils, however, feedback control is more difficult. As proven by Earnshaw's theorem, it is not possible to achieve a stable magnetic trap in 3D by static permanent magnets. Here, we report a stable 2D magnetic force trap by an array of permanent magnets to control a millirobot. The trap is located in an open space with a tunable distance to the magnet array in the range of 20 - 120mm, which is relevant to human anatomical scales. The design is achieved by a novel GPU-accelerated optimization algorithm that uses mean squared error (MSE) and Adam optimizer to efficiently compute the optimal angles for any number of magnets in the array. The algorithm is verified using numerical simulation and physical experiments with an array of two magnets. A millirobot is successfully trapped and controlled to follow a complex trajectory. The algorithm demonstrates high scalability by optimizing the angles for 100 magnets in under three seconds. Moreover, the optimization workflow can be adapted to optimize a permanent magnet array to achieve the desired force vector fields.

Untethered soft robots are essential for advancing the real-world deployment of soft robotic systems in diverse and multitasking environments. Inspired by soft-bodied inchworm, we present a fully untethered soft robot with a curved, flexible structure actuated by magnetic forces. The robot has a total mass of 102.63 g and demonstrates multimodal locomotion, achieving a maximum walking speed of 3.74 cm/s and a swimming speed of 0.82 cm/s. A compact and lightweight onboard control circuit enables wireless command transmission, while an integrated camera provides environmental perception. Through structural optimization and system-level integration, the robot successfully performs walking, steering, swimming, and payload transport without reliance on external infrastructure. The robot's dynamic performance and locomotion capabilities are systematically validated through experimental characterization. Introduction In nature, locomotion is enabled by deformable bodies, inspiring soft robots that mimic biological motion and functionality.

-- Vision-Language-Action (VLA) models have emerged as a prominent research area, showcasing significant potential across a variety of applications. However, their performance in endoscopy robotics, particularly endoscopy capsule robots that perform actions within the digestive system, remains unexplored. The integration of VLA models into endoscopy robots allows more intuitive and efficient interactions between human operators and medical devices, improving both diagnostic accuracy and treatment outcomes. By processing interleaved visual inputs, and textual instructions, CapsDT can infer corresponding robotic control signals to facilitate endoscopy tasks. In addition, we developed a capsule endoscopy robot system, a capsule robot controlled by a robotic arm-held magnet, addressing different levels of four endoscopy tasks and creating corresponding capsule robot datasets within the stomach simulator . Comprehensive evaluations on various robotic tasks indicate that CapsDT can serve as a robust vision-language generalist, achieving state-of-the-art performance in various levels of endoscopy tasks while achieving a 26.25% success rate in real-world simulation manipulation. I. INTRODUCTION Endoscopy, for both diagnostic and therapeutic interventions, provides direct visualization and treatment capabilities within the gastrointestinal (GI) tract [1], [2], [3].

Tactile sensing and the manipulation of delicate objects are critical challenges in robotics. This study presents a vision-based magnetic-actuated whisker array sensor that integrates these functions. The sensor features eight whiskers arranged circularly, supported by an elastomer membrane and actuated by electromagnets and permanent magnets. A camera tracks whisker movements, enabling high-resolution tactile feedback. The sensor's performance was evaluated through object classification and grasping experiments. In the classification experiment, the sensor approached objects from four directions and accurately identified five distinct objects with a classification accuracy of 99.17% using a Multi-Layer Perceptron model. In the grasping experiment, the sensor tested configurations of eight, four, and two whiskers, achieving the highest success rate of 87% with eight whiskers. These results highlight the sensor's potential for precise tactile sensing and reliable manipulation.

Permanent magnet tracking using the external sensor array is crucial for the accurate localization of wireless capsule endoscope robots. Traditional tracking algorithms, based on the magnetic dipole model and Levenberg-Marquardt (LM) algorithm, face challenges related to computational delays and the need for initial position estimation. More recently proposed neural network-based approaches often require extensive hardware calibration and real-world data collection, which are time-consuming and labor-intensive. To address these challenges, we propose MobilePosenet, a lightweight neural network architecture that leverages depthwise separable convolutions to minimize computational cost and a channel attention mechanism to enhance localization accuracy. Besides, the inputs to the network integrate the sensors' coordinate information and random noise, compensating for the discrepancies between the theoretical model and the actual magnetic fields and thus allowing MobilePosenet to be trained entirely on theoretical data. Experimental evaluations conducted in a \(90 \times 90 \times 80\) mm workspace demonstrate that MobilePosenet exhibits excellent 5-DOF localization accuracy ($1.54 \pm 1.03$ mm and $2.24 \pm 1.84^{\circ}$) and inference speed (0.9 ms) against state-of-the-art methods trained on real-world data. Since network training relies solely on theoretical data, MobilePosenet can eliminate the hardware calibration and real-world data collection process, improving the generalizability of this permanent magnet localization method and the potential for rapid adoption in different clinical settings.

Precise manipulation of liquid marbles has significant potential in various applications such as lab-on-a-chip systems, drug delivery, and biotechnology and has been a challenge for researchers. Ferrofluid marble (FM) is a marble with a ferrofluid core that can easily be manipulated by a magnetic field. Although FMs have great potential for accurate positioning and manipulation, these marbles have not been precisely controlled in magnetic digital microfluidics, so far. In this study for the first time, a novel method of magnetic actuation is proposed using a pair of Helmholtz coils and permanent magnets. The governing equations for controlling the FM position are investigated, and it is shown that there are three different strategies for adjusting the applied magnetic force. Then, experiments are conducted to demonstrate the capability of the proposed method. To this aim, different magnetic setups are proposed for manipulating FMs. These setups are compared in terms of energy consumption and tracking ability across various frequencies. The study showcases several applications of precise FM position control, including controllable reciprocal positioning, simultaneous position control of two FMs, the transport of non-magnetic liquid marbles using the FMs, and sample extraction method from the liquid core of the FM.

Robotic magnetic manipulation offers a minimally invasive approach to gastrointestinal examinations through capsule endoscopy. However, controlling such systems using external permanent magnets (EPM) is challenging due to nonlinear magnetic interactions, especially when there are complex navigation requirements such as avoidance of sensitive tissues. In this work, we present a novel trajectory planning and control method incorporating dynamics and navigation requirements, using a single EPM fixed to a robotic arm to manipulate an internal permanent magnet (IPM). Our approach employs a constrained iterative linear quadratic regulator that considers the dynamics of the IPM to generate optimal trajectories for both the EPM and IPM. Extensive simulations and real-world experiments, motivated by capsule endoscopy operations, demonstrate the robustness of the method, showcasing resilience to external disturbances and precise control under varying conditions. The experimental results show that the IPM reaches the goal position with a maximum mean error of 0.18 cm and a standard deviation of 0.21 cm. This work introduces a unified framework for constrained trajectory optimization in magnetic manipulation, directly incorporating both the IPM's dynamics and the EPM's manipulability.

Soft robotic manipulators are generally slow despite their great adaptability, resilience, and compliance. This limitation also extends to current soft robotic micromanipulators. Here, we introduce FilMBot, a 3-DOF film-based, electromagnetically actuated, soft kinematic robotic micromanipulator achieving speeds up to 2117 $\deg$/s and 2456 $\deg$/s in $\alpha$ and $\beta$ angular motions, with corresponding linear velocities of 1.61 m/s and 1.92 m/s using a 4-cm needle end-effector, and 1.57 m/s along the Z axis. The robot can reach ~1.50 m/s in path-following tasks, operates at frequencies up to 30 Hz, and remains functional up to 50 Hz. It demonstrates high precision (~6.3 $\mu$m, or ~0.05% of its workspace) in small path-following tasks. The novel combination of the low-stiffness soft kinematic film structure and strong electromagnetic actuation in FilMBot opens new avenues for soft robotics. Furthermore, its simple construction and inexpensive, readily accessible components could broaden the application of micromanipulators beyond current academic and professional users.

Magnetism is widely used for the wireless localization and actuation of robots and devices for medical procedures. However, current static magnetic localization methods suffer from large required magnets and are limited to only five degrees of freedom due to a fundamental constraint of the rotational symmetry around the magnetic axis. We present the small-scale magneto-oscillatory localization (SMOL) method, which is capable of wirelessly localizing a millimeter-scale tracker with full six degrees of freedom in deep biological tissues. The SMOL device uses the temporal oscillation of a mechanically resonant cantilever with a magnetic dipole to break the rotational symmetry, and exploits the frequency-response to achieve a high signal-to-noise ratio with sub-millimeter accuracy over a large distance of up to 12 centimeters and quasi-continuous refresh rates up to 200 Hz. Integration into real-time closed-loop controlled robots and minimally-invasive surgical tools are demonstrated to reveal the vast potential of the SMOL method.

This paper introduces a novel magnetic navigation system for cardiac ablation. The system is formed from two key elements: a magnetic ablation catheter consisting of a chain of spherical permanent magnets; and an actuation system comprised of two cart-mounted permanent magnets undergoing pure rotation. The catheter design enables a large magnetic content with the goal of minimizing the footprint of the actuation system for easier integration with the clinical workflow. We present a quasi-static model of the catheter, the design of the actuation units, and their control modalities. Experimental validation shows that we can use small rotating magnets (119mm diameter) to reach cardiac ablation targets while generating clinically-relevant forces. Catheter control using a joystick is compared with manual catheter control. blue While total task completion time is similar, smoother navigation is observed using the proposed robotic system. We also demonstrate that the ball chain can ablate heart tissue and generate lesions comparable to the current clinical ablation catheters.