Author name cluster

Yaowei Liu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

IROS Conference 2025 Conference Paper

Automated Dual-Micropipette Coordination Microinjection for Batch Zebrafish Larvae Based on Pose Estimation

Can Wang
Rongxin Liu
Huiying Gong
Zengshuo Wang
Lu Zhou
Yaowei Liu
Xin Zhao 0010
Mingzhu Sun

Zebrafish are widely used in the biomedical field, as an ideal model for microinjection. In automated zebrafish microinjection, posture adjustment is the first and key step, which takes a lot of skill, and injection success assessment is a challenging task. Constrained by these two aspects, it is difficult to further enhance the efficiency and success rate of injection. In this study, we propose an automated dual-micropipette coordination microinjection system. Zebrafish are randomly arranged in our system, reducing the operational difficulty, and the yolk is positioned using a pose estimation algorithm, followed by injection accomplished with dual-micropipette. Due to the reduction of posture adjustment time by half, the proposed system achieves the shortest injection time of 15. 2s. Moreover, the simplicity of the system and the ease of operation contribute to the clinical feasibility of our system.

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IROS Conference 2025 Conference Paper

Modeling and Simulation of Single-micropipette Cell Rotation for Imitation Learning

Zefu Wang
Yuchen Hua
Huiying Gong
Yujie Zhang
Zhanli Yang
Yaowei Liu
Xin Zhao
Mingzhu Sun

Cell rotation plays a crucial role in micromanipulation. Among manual cell rotation techniques, single-micropipette cell rotation is widely adopted due to its high efficiency and flexibility. However, there is currently no method capable of achieving automated single-micropipette cell rotation. In this study, we developed the first three-dimensional (3D) simulation system for single-micropipette cell rotation. Based on this simulation system, we successfully achieved single-micropipette cell rotation imitation learning (IL) for the first time. Specifically, we first analyze the forces acting on cells in the fluid, establishing a dynamic model that describes the cell's behavior in response to the flow velocity at the holding micropipette's orifice, the relative position of the micropipette, and time. We then developed the cell rotation simulation environment by discretizing the model and designing the simulation's cell and holding micropipette models based on real-world conditions. Finally, we designed a network architecture for IL using this model, achieving single-micropipette cell rotation in simulation. The results demonstrate that the simulation system exhibits a relative error range of 5. 34% to 12. 21% compared to real-world experiments, indicating a high degree of accuracy. Additionally, the single-micropipette cell rotation task achieved a success rate of 69% with an average completion time of 17. 13 seconds, closely matching the expert data's average time of 17. 69 seconds, confirming the feasibility of the simulation system.

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ICRA Conference 2023 Conference Paper

Automatic Cell Rotation Method Based on Deep Reinforcement Learning

Huiying Gong
Yujie Zhang
Yaowei Liu
Qili Zhao
Xin Zhao 0010
Mingzhu Sun

Cell rotation is widely used to adjust cell posture in sub-cellular micromanipulations. The trajectory planning of the injection micropipette is needed, so that the cells can be rotated with the minimum deformation to reduce cell damage and keep cell viability. Due to the uncertainty of cell properties and manipulation environment, it is difficult to identify the parameters of the mechanical models in traditional robotic cell rotation methods. In this paper, deep reinforcement learning is introduced into cell manipulation for the first time to perform trajectory planning of the micropipette. We first abstract the cell rotation process by using the mechanical model and microscopic vision techniques and build a cell rotation simulation environment. Then we design a reward function by combining various factors of cell rotation and implement a reinforcement learning framework based on deep Q-learning (DQL). Finally, we train the cell rotation process based on the deep reinforcement learning algorithm. The simulation results indicate the proposed DQL agent achieved an average success rate of 97% without useless exploration. Moreover, the proposed method rotated the cells in a way that causes less mechanical damage than humans, demonstrating the DRL ability for cell rotation with high efficiency and low cell damage.

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IROS Conference 2022 Conference Paper

Simultaneous Depth Estimation and Localization for Cell Manipulation Based on Deep Learning

Zengshuo Wang
Huiying Gong
Ke Li 0026
Bin Yang
Yue Du
Yaowei Liu
Xin Zhao 0010
Mingzhu Sun

Visual localization, which is a key technology to realize the automation of cell manipulation, has been widely studied. Since the depth of field of the microscope is narrow, the planar localization and depth estimation are usually coupled together. At present, most methods adopt the serial working mode of focusing first and then planar localization, but they usually do not have good real-time performance and stability. In this paper, a simultaneous depth estimation and localization network was developed for cell manipulation. The network takes a focused image and a defocus-offset image as inputs, and outputs the defocus in the depth direction and the offset in the plane at the same time after going through defocus-offset information extraction, defocus classification mapping and offset regression mapping. To train and test our network, we also create two datasets: An Adherent Cell dataset and an Injection Micropipette dataset. The experimental results demonstrated that the proposed method achieves the detection of all test samples with a frame rate of more than 40Hz, and the maximum errors of depth estimation and localization are $\boldsymbol{2. 44\mu m}$ and $\boldsymbol{0. 49\mu m}$, respectively. The proposed method has good stability, which is mainly reflected in its strong generalization ability and anti-noise ability.

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IROS Conference 2017 Conference Paper

Automated cell transportation for batch-cell manipulation

Xuefeng Wang 0003
Yaowei Liu
Shibao Li
Maosheng Cui
Mingzhu Sun
Xin Zhao 0010

Batch-cell manipulation is a key technology in biological applications. Robotic manipulation has important significance to improve the operation success rate and reduce the technical threshold, but the problem of inefficiency still exists in batch-cell experiments. In this paper, an automated cell transportation system is designed for batch-cell manipulation. It has some technical aspects such as a cell groove to contain the cells, the micromanipulator and motor stage control methods, and computer vision algorithms. Since the cells are arranged in a line in the groove, the transportation system improves the efficiency of finding the cells in the petri dish. Furthermore, the minimum pressure to drag and release the cell are analyzed theoretically, so that the other cells will not be affected when manipulating one cell. The visual algorithms to detect the cell position and cell holding state are evaluated by porcine oocyte. Experimental results show both algorithm has high success rates: 96% and 100%. Finally, cell rotating experiments are introduced to verify the effectiveness of the transportation system. The average transfer efficiency has been improved by 20% compared to manual operation. The results show that this system can be used in many manipulations.

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