# Robotic Manipulation of Cumulus-Oocyte Complexes for Cumulus Cell Removal
## 1 Introduction
designed a microfluidic device consisting of a digesting chamber and a pit array, where COCs go through a long channel filled with hyaluronidase for cumulus cell removal and then fall into pits due to gravity for fertilization [12]. [PERSON] et al. proposed an active microfluidic device with multiple inlet ports to remove cumulus cells. By using multiple inlet ports, the device avoided clogging COCs during cumulus cell removal [13]. [PERSON] and [PERSON] developed a vortex device to remove cumulus cells by producing a vortex around the oocytes [14]. We previously proposed a robotic system that removed cumulus cells by repeatedly aspirating and dispensing the COCs via motion control of the COCs inside the micropipette [15]. However, these devices focused on the pressure control of the flow inside the microfluidic channels or micropipettes. The assessment of oocyte maturity before cumulus cell removal and the quantification of the completeness of cumulus cell removal were ignored. Furthermore, the mass of the COCs during cumulus cell removal was considered to be constant, resulting in large positioning errors of the COCs inside the channels or micropipettes. Several methods have been developed to assess oocyte maturity. Since the oocyte is considered mature when the polar body is extruded from the oocyte and clearly visible [16], [PERSON] et al. introduced an edge detection method to identify the presence and orientation of the polar body of the oocyte that is rotated by the injection micropipette [17]. [PERSON] et al. proposed an improved support vector machine method for the detection of the polar body in order to aid in the removal of the polar bodies from the oocyte [18]. [PERSON] et al. used the deep neural network method to robustly detect polar bodies with various sizes and shapes for orientation control of the oocyte [19]. However, the existing methods only detect polar bodies of oocytes after cumulus cell removal. In this scenario, the cumulus cells surrounding the oocytes will degrade the detection performance of the current methods. To accurately position the cells at the target position inside the micropipette, [PERSON] et al. developed a sliding controller to position the fibroblast cell with less than 20 \(\upmu\)m in diameter at the target position within the microfluidic channel during organelle biopsy, enabling the 3D confinement of small organelles for extraction [20]. [PERSON] et al. designed a PID controller to decelerate oocytes \(\sim\)100 \(\upmu\)m in diameter to the target position inside the micropipette during vitrification, preventing the oocyte from becoming lost out of the field of view [21]. However, the existing methods only consider cells with constant mass. During aspiration and deposition, the mass of COC varies as the cumulus cells are removed. Ignoring the mass change in the COC will lead to the large positioning errors. To assess whether cumulus cells have been completely removed, machine learning models for cumulus cell detection have been proposed. [PERSON] et al. used a U-shaped network model to detect cumulus cells and classify the removal completeness [22]. [PERSON] et al. introduced the model based on a faster region-based convolutional neural network to assess the removal completeness [23]. However, the existing methods focus on the qualitative assessment of removal completeness. Quantitative methods are needed to accurately calculate the number of remaining cumulus cells to ensure complete removal. In this work, a physical robotic system was developed to automatically remove cumulus cells. The challenges to overcome are as follows. (1) It is difficult to assess the maturity of an oocyte via the polar body detection, as the polar body is occluded by the surrounding cumulus cells. (2) The mass of the COC varies during micropipette aspiration and dispensing, leading to the large positioning errors of the COC inside the micropipette. Therefore, it is easy for the COC to become lost after it enters deep into the micropipette using existing aspiration control.