Microswarms face challenges in precise delivery within dynamic biological fluids due to fluid disturbances and limited operational intuitiveness. Current approaches insufficiently utilize the operator’s perceptual awareness and interactive decision-making capabilities, particularly in complex tasks that require a balance between flexibility and precision. In this study, we propose a haptic-assisted magnetic actuation control strategy, establishing a human-in-the-loop control framework. The haptic perception system provides the operator with haptic feedback reflecting the interactions between the microswarm and the environment. A real-time tracking system monitors the position and pattern of the controlled microswarm in remote environments, and transmits this information to the control system for decision-making. After characterizing the magnetic field parameters and magnetic nanoparticles, we have achieved real-time navigation and morphology modulation of the microswarm in dynamic flow conditions and three-dimensional (3D) space. Comparative experiments under various flow rate conditions demonstrate that the haptic-assisted strategy enhances microswarm control stability and precision across different flow regimes. Moreover, the human-machine collaboration mechanism improves delivery success rates (97%) under sudden disturbances compared to preprogrammed automated control and purely manual control, validating its potential for applications in complex biomedical scenarios. Our work provides a haptic-assisted microswarm control method in dynamic conditions, expanding an adaptive microswarm control strategy in complex biomedical environments.

Magnetic actuation is a promising approach in the robotic manipulation field, enabling wireless manipulation for small-scale operations. However, selective three-dimensional (3D) manipulation of multiple magnetic microrobots under global magnetic fields remains a challenge. This paper presents a dynamic magnetic modeling and vision-guided control strategy to realize 3D manipulation of magnetic microrobots, including patch-robot-assisted collective delivery and microrobot screening. An impedance regulation-based position control method is proposed, leveraging theoretical analysis of electromagnetic forces and fluid drag to accommodate microrobots with diverse morphologies. Through trajectory motion experiments, our control strategy ensures that the mean absolute errors (MAE) of the microrobots are consistently below 200 μm. By utilizing patch robot adhesion and differential magnetic responses among the microrobots, this strategy enables selective manipulation and collective sorting in a 3D space. Applications in patch-robot-assisted delivery, collective sorting and screening are validated. The proposed approach advances magnetic microrobot control by enabling spatially selective operations critical for biomedical tasks.

Swarming microrobots offer great promise for targeted delivery in biofluidic environments. However, current approaches insufficiently utilize the operator’s perceptual awareness and interactive decision-making capabilities. This work proposes a real-time navigation and control strategy with haptic feedback for delivering magnetic microswarm, in which the haptic feedback system provides microswarm-environment interaction to the operator. The real-time tracking system continuously monitors the position and shape of the microswarm in the remote environment, transmitting data to the control system for decision-making. This integration can achieve real-time perception and feedback of the microswarm’s state and motion process. Moreover, the strategy successfully demonstrates navigation and shape-adaptive regulation of the microswarm under static, downstream and three-dimensional (3D) upstream flow conditions. The experimental results show that the haptic feedback enables real-time trajectory and velocity adjustments during navigation, improving control robustness and delivery accuracy. Our work expands a haptic feedback-enabled microswarm control in dynamic conditions, providing an adaptive swarm control strategy in complex biomedical environments.

Microrobotic swarms have shown promising features due to their collective and flexible behaviours, while achieving precise swarm control and autonomous navigation in complex environments remains a challenge. Here, we propose a Transformer-based reinforcement learning strategy that integrates Proximal Policy Optimization for autonomous swarm control in obstacle environments. By combining domain randomization, this strategy enables direct transfer from simulation to real-world without fine tuning. Experimental results demonstrate robust control performance in avoiding static obstacles and tracking the dynamic target, which is not validated in training. The swarm autonomously navigates and adjusts its velocity and trajectory in obstacle environments with an intact swarm pattern. Our work presents a scalable strategy for the deployment of microrobotic swarms with adaptive navigation capability through complex, constrained environments.

Magnetic microrobots are showing great potential in micromanipulation due to the capability of motion control under external fields. However, achieving selective control of magnetic microrobots in three-dimensional (3D) space using global magnetic fields still presents a challenge. In this work, we propose a selective control strategy based on a movable electromagnetic coil system, incorporating a mass-spring-damping model to achieve precise control of cell microrobots in 3D space. By combining theoretical analysis with vision-based feedback, experiments are demonstrated in different scenarios, including step climbing and ring traversal, validating the control capability in different environments. Furthermore, by utilizing the differences in magnetic responses among cell microrobots, this strategy enables selective manipulation of multiple cell microrobots, demonstrating real-time sorting manipulation in a 3D space. Our work presents a strategy that can be applied to selectively manipulate magnetic microrobots in complex environments.

Consider a large social network with possibly severe degree heterogeneity and mixed-memberships. We are interested in testing whether the network has only one community or there are more than one communities. The problem is known to be non-trivial, partially due to the presence of severe degree heterogeneity. We construct a class of test statistics using the numbers of short paths and short cycles, and the key to our approach is a general framework for canceling the effects of degree heterogeneity. The tests compare favorably with existing methods. We support our methods with careful analysis and numerical study with simulated data and a real data example.