One of the typical purposes of using lower-limb exoskeleton robots is to provide assistance to the wearer by supporting their weight and augmenting their physical capabilities according to a given task and human motion intentions. The generalizability of robots across different wearers in multiple tasks is important to ensure that the robot can provide correct and effective assistance in actual implementation. However, most lower-limb exoskeleton robots exhibit only limited generalizability. Therefore, this paper proposes a human-in-the-loop learning and adaptation framework for exoskeleton robots to improve their performance in various tasks and for different wearers. To suit different wearers, an individualized walking trajectory is generated online using dynamic movement primitives and Bayes optimization. To accommodate various tasks, a task translator is constructed using a neural network to generalize a trajectory to more complex scenarios. These generalization techniques are integrated into a unified variable impedance model, which regulates the exoskeleton to provide assistance while ensuring safety. In addition, an anomaly detection network is developed to quantitatively evaluate the wearer's comfort, which is considered in the trajectory learning procedure and contributes to the relaxation of conflicts in impedance control. The proposed framework is easy to implement, because it requires proprioceptive sensors only to perform and deploy data-efficient learning schemes. This makes the exoskeleton practical for deployment in complex scenarios, accommodating different walking patterns, habits, tasks, and conflicts. Experiments and comparative studies on a lower-limb exoskeleton robot are performed to demonstrate the effectiveness of the proposed framework.

The mobile robot kinematic model is a nonlinear affine system, which is constrained by velocity and acceleration limits. Therefore, the traditional control methods may not solve the tracking problem because of the physical constraint. In this paper, we present the nonlinear model predictive control (NMPC) algorithm to track the desired trajectory based on neural-dynamic optimization. In the proposed algorithm, the NMPC scheme utilizes a new neural network named the varying-parameter convergent differential neural network (VPCDNN) which is a Hopfifield-neural network structure with respect to the differential equation theory to solve the quadratic programming (QP) problem. The new network structure converges to the global optimal solution and it is more efficient than traditional numerical methods. In the simulation, we verify that the proposed method is able to successfully track reference trajectories with a two-wheel mobile robot. The experimental validation has been conducted in simulation and the results show that the proposed method is able to precisely track the trajectory maintaining a high robustness based on the VPCDNN solver.

Embryo vitrification is a fundamental technology utilized in assisted reproduction and fertility preservation. Vitrification involves sequential loading and unloading of cryoprotectants (CPAs) with strict time control, and transferring the embryo in a minimum CPA droplet to the vitrification straw. However, manual operation still cannot effectively avoid embryo loss, and the existing automatic vitrification systems have insufficient system reliability, and operate differently from clinical vitrification protocol. Through collaboration with in vitro fertilization (IVF) clinics, we are in the process realizing a robotic system that can automatically conduct the embryo vitrification process, including the pretreatment with CPAs, transfer of embryo to the vitrification straw, and cryopreservation with liquid nitrogen ( $\rm LN_{2}$ ). An open microfluidic chip (OMC) was designed to accommodate the embryo during the automatic CPAs pretreatment process. The design of two chambers connected by a capillary gap facilitated solution exchange around the embryo, and simultaneously reduced the risk of embryo loss in the flow field. In accordance to the well-accepted procedure and medical devices in manual operation, we designed the entire vitrification protocol, as well as the robotic prototype. In a practical experiment using mouse embryos, our robotic system showed a 100 $\%$ success rate in transferring and vitrifying the embryos, achieved comparable embryo survival rates (90.9 $\%$ versus 94.4 $\%$ ) and development rates (90.0 $\%$ versus 94.1 $\%$ ), when compared with the manual group conducted by the senior embryologist. With this study, we aim to facilitate the standardization of clinical vitrification from manual operation to a more efficient and reliable automated process.

Oocyte vitrification technology is widely used for assisted reproduction and fertility preservation, which requires precise washing sequences and timings of cryoprotectant agents (CPAs) treatment to relieve the osmotic shock to cells. The gold standard Cryotop method is extensively used in oocyte vitrification and is currently the most commonly used method in reproductive centers. However, the Cryotop method requires precise and complex manual manipulation by an embryologist, whose proficiency directly determines the effect of vitrification. Therefore, in this study, an automatic microfluidic system consisting of a novel open microfluidic chip and a set of automatic devices was established as a standardized operating protocol to facilitate the conventional manual Cryotop method and minimize the osmotic shock applied to the oocyte. The proposed open microfluidic system could smoothly change the CPA concentration around the oocyte during vitrification pretreatment, and transferred the treated oocyte to the Cryotop with a tiny droplet. The system better conformed to the operating habits of embryologists, whereas the integration of commercialized Cryotop facilitates the subsequent freezing and thawing processes. With standardized operating procedures, our system provides consistent treatment effects for each operation, leading to comparable survival rate, mitochondrial membrane potential (MMP) and reactive oxygen species (ROS) level of oocytes to the manual Cryotop operations. The vitrification platform is the first reported microfluidic system integrating the function of cells transfer from the processing chip, which avoids the risk of cell loss or damage in a manual operation and ensures the sufficient cooling rate during liquid nitrogen (${\mathrm{LN}}_2$) freezing. Our study demonstrates significant potential of the automatic microfluidic approach to serve as a facile and universal solution for the vitrification of various precious cells.

Embryo vitrification is an essential technique for in vitro fertilization (IVF), which avoids the formation of ice crystals during cryopreservation by dehydrating cells in cryoprotectant agents (CPAs) and ultrarapidly freezing them in liquid nitrogen ( $\rm LN_{2}$ ). In manual vitrification, the embryo will be repeatedly aspirated and released in different CPAs and finally transferred onto the vitrification straw under the microscope, which is time-consuming and requires intensive training of skilled operators. Robotic cell manipulation has been considered the solution to enhance the processing efficiency while reducing the failure rate for the manual vitrification protocols; however, the time-critical treatment with CPAs and transfer of the embryo to the straw still remain challenging in ongoing studies. In this article, we report a microfluidics-enabled robotic system for the emerging automatic embryo vitrification. Based on the strategy of microfluidic manipulation in our early works, an open microfluidic chip was designed to confine the embryo within a specific region while manipulating the surrounding flow field for solution exchange through the capillary effect. The embryo chamber was zoned into safe and danger zones based on the numerically simulated local velocity toward the capillary gap, which represents the risk of embryo loss and damage. A vision-based switch controller was adopted to manipulate the embryo trajectory during the solution exchange process, which selected the optimized flow rate according to the zoning of real-time embryo. The challenge of embryo transfer was addressed by a two-stage capillary valve, which switches open and allows the embryo to pass through under a certain pressure. The vitrification experiments with porcine embryos validate the reliable positioning of the embryo in each step, the satisfactory success rate of the robotic system, and the high quality of the robotic-vitrified embryos after thawing. Integrated with new manipulation strategies, our intelligent mechatronics system provides a promising solution for IVF featuring an embryologist-centered configuration and standardized robotic operation.

Micromanipulation systems leverage automation and robotic technologies to improve the precision, repeatability, and efficiency of various tasks at the microscale. However, current approaches are typically limited to specific objects or tasks, which necessitates the use of custom tools and specialized grasping methods. This letter proposes a novel non-contact micromanipulation method based on optoelectronic technologies. The proposed method utilizes repulsive dielectrophoretic forces generated in the optoelectronic field to drive a microrobot, enabling the microrobot to push the target object in a cluttered environment without physical contact. The non-contact feature can minimize the risks of potential damage, contamination, or adhesion while largely improving the flexibility of manipulation. The feature enables the use of a general tool for indirect object manipulation, eliminating the need for specialized tools. A series of simulation studies and real-world experiments—including non-contact trajectory tracking, obstacle avoidance, and reciprocal avoidance between multiple microrobots—are conducted to validate the performance of the proposed method. The proposed formulation provides a general and dexterous solution for a range of objects and tasks at the micro scale.