## 4 Discussion  
In this paper, we proposed an automated pipeline for synthetic training data generation and fine-tuning of GQCNN models for task-specific grasp planning in robotic assembly \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Success Rate (Task)** & **Success Rate (Pick)** & **Success Rate (Place)** \\ \hline Dex-Net 2.0 GQCNN & 0.65 & 1.0 & 0.65 \\ Fine-tuned GQCNN & **0.9** & 1.0 & 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation of our fine-tuned GQCNN against the Dex-Net 2.0 pre-trained GQCNN for depth images generated from a camera with the same extrinsic parameters as in the training setup. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Success Rate (Task)** & **Success Rate (Pick)** & **Success Rate (Place)** \\ \hline Dex-Net 2.0 GQCNN & 0.5 & 1.0 & 0.5 \\ Fine-tuned GQCNN & **0.55** & 1.0 & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of our fine-tuned GQCNN against the Dex-Net 2.0 pre-trained GQCNN for depth images generated from a camera with the different extrinsic parameters from the training setup. scenarios. Our method assumes that the object models and assembly information, such as assembly order and the relative pose of objects in the assembly, are known in advance. We generated robust grasps for picking the object using Graspit! and automatically classified these grasps into valid and invalid ones based on their similarity to grasps from another set, generated from Graspit! for the placing of the object. For the set of valid and invalid grasps, we automatically generated synthetic depth images using Blender, and we used these synthetic images to fine-tune a GQCNN model. As a result, the fine-tuned GQCNN model learned to predict grasps with a high probability of success, which is feasible for picking the object and placing it into the assembly as well. We evaluated our method on a simple simulated scenario and showed that a fine-tuned GQCNN can significantly outperform the original Dex-Net 2.0 pre-trained model, in the context of grasp planning for a specific task. Our method can quickly be applied to train GQCNN models for a flexible robotic assembly scenario. One limitation of the proposed method is that in Graspit!, only the robot gripper is included during determining the pick and place grasps. While this may work well for simple scenes of tabletop assembly, it may result in a fine-tuned GQCNN, which predicts infeasible grasps in a highly cluttered scene due to the robot's self-collisions or interferences with the environment during task execution. A possible future solution would be to use MTC for determining valid and invalid grasps, where collision for the whole robot arm could be considered instead of the currently used proximity-based method. During our experiments, we concluded that training data generated with only a single camera pose results in a fine-tuned GQCNN that loses its edge against the original pre-trained model in scenarios when the camera pose is different from the one used for the generation of the training data. In the future, the robustness of fine-tuned GQCNNs could be examined by including randomization in the camera pose for generating the training data. Additionally, this paper focuses on the geometric evaluation of predicted grasp poses (via collision detection), but an evaluation in a dynamic environment could also be performed in the future. The supporting information can be downloaded at: [[https://github.com/ABC-iRobotics/Task-Specific-Grasp-Planning-GQCNN](https://github.com/ABC-iRobotics/Task-Specific-Grasp-Planning-GQCNN)]([https://github.com/ABC-iRobotics/Task-Specific-Grasp-Planning-GQCNN](https://github.com/ABC-iRobotics/Task-Specific-Grasp-Planning-GQCNN)). Conceptualization, A.I.K. and P.G. ; methodology, A.I.K. ; software, A.I.K. ; validation, A.I.K. ; writing--original draft preparation, A.I.K. ; writing--review and editing, A.I.K. and P.G. ; visualization, A.I.K. ; supervision, P.G. ; project administration, P.G. ; funding acquisition, P.G.