## 3 Results  
### Experimental Setup  
In our experiments, a simple peg-in-hole-like insertion task is considered, where we evaluate the quality of predicted grasps from different GQCNN models according to the success rate of collision-free motion plans. The experimental setup can be seen in Figure 4 (as visualized by RViz). The peg object in the experiments is made up of two box primitives, one of which is sized 10 cm in the X direction and 1 cm in both Y and Z directions, with the origin being the geometric center of the box, the other one is sized 3 cm in X and 2 cm in Y and Z directions and is located 3.5 cm in the X direction. In the setup, the object is lying on its side (the Z axis of the object frame pointing upwards) on the surface of a table. The table is simply represented as a flat surface.  
Since the 3D grasp pose predicted by the GQCNNs is oriented according to the line between the depth camera and the grasp point, we included an additional platform for placing in MTC to avoid unnecessary collisions between the robot arm and the table. Inside Graspit! we simply use the \"table\" object instead. For the gripper (our solution is based on the Panda Hand model provided here: [[https://github.com/[PERSON]/grasppit-pkgs/issues/55#issue-515423230](https://github.com/[PERSON]/grasppit-pkgs/issues/55#issue-515423230)]([https://github.com/[PERSON]/grasppit-pkgs/issues/55#issue-515423230](https://github.com/[PERSON]/grasppit-pkgs/issues/55#issue-515423230)), Accessed: 12 December 2022), we defined the virtual contacts using the user interface of Graspit! and modified the inventor files to include appropriate scaling so the size of the gripper in Graspit! matches the real-life gripper. We ran the Graspit grasp planning a total of 3 times for pick grasps and 5 times for place grasps, from which we acquired 23-26 pick grasps, and around 30 place grasps for each frame after the initial filtering (removing multiples of pick grasps and place grasps or grasps with high contact energy). Based on geometric proximity, 5 of the pick grasps were classified as valid, and the rest were classified as invalid (see Figure 2).  
The fine-tuning dataset was generated using the first 20 frames (the object pose is randomized for each frame) from Blender. For these, a total of 489 pick grasps were classified as either valid or invalid. During converting the data into Dex-Net 2.0 format, we added the additional 12 invalid grasps for each of the 489 original ones to enforce precise gripper orientation prediction. This resulted in a total of 6357 grasps, of which the ratio of grasps with positive labels was around 1.57%.  
On this dataset, we fine-tuned the Dex-Net 2.0 pre-trained GQCNN model, for 60 epochs using a batch size of 64. This fine-tuned model is evaluated against the original Dex-Net 2.0 pre-trained model. The success rate of the pick-place operations was evaluated by comparing the original and the fine-tuned GQCNN. For this evaluation, we used 20 additional frames (not used for training or validation) that use the same camera placement as in the setup for generating the training dataset, and 40 other frames generated using a camera placement different from the training setup. The findings of these evaluations are reported separately to highlight the method's sensitivity to the variation of the setup.  
Figure 4: Experimental setup used for evaluation: (**a**) scene setup with the robot, object camera, and table, (**b**) planned grasps which do not result in collisions are evaluated as successful, (**c**) grasps that result in collisions between the robot and the table are evaluated as unsuccessful.