# Robotic Grasping of Unknown Objects Based on Deep-Learning Based Feature Detection
## 3 Experiments
### Discussion  
The proposed algorithm demonstrates a superior ability to grasp a large variety of unseen objects. Rectangular-shaped objects like the game console (Figure 19, Object 34) are easily graspable due to their obvious straight edges. Even if they have some stray components like loose wires found in the battery pack (Figure 19, Object 23) and the electrical component (Figure 19, Object 39), the algorithm is able to ignore them and still grasp along the straight edges. Some items with smaller straight edges like the set of keys (Figure 19, Object 12) and the die (Figure 19, Object 35) are also identifiable by the algorithm. It is worth noting that the algorithm works particularly well with handled objects like the kitchen knife (Figure 19, Object 2) and the spatula (Figure 19, Object 27), attributable to the property that handles are usually straight and thick, which makes for very prominent parallel edges. Other odd-shaped objects like the Swiss Army Knife (Figure 19, Object 31) and the wine opener (Figure 19, Object 28) are also graspable, as they have obvious parallel edges despite the overall irregular shape of the object. The Swiss Army Knife, in particular, showcases the advantage of the algorithm in terms of flexibility. Being a highly transformable object, the Swiss Army Knife was changed in shape significantly over the course of testing, as illustrated in Figure 17. The algorithm aims for the middle of the handle, as in Figure 20a, where possible. When the corkscrew tool is opened at the middle of the handle, the algorithm automatically shifts its grip to the side of the Swiss Army Knife to avoid collision with the corkscrew tool in Figure 20b. The translucent box (Figure 19, Object 29) showcases the maximum gripper distance logic of the algorithm. Being a square box, it has obvious straight edges. However, this box was deliberately chosen, as it has a length of about 12 cm compared to the gripper's maximum opening of 10 cm. The algorithm is able to determine that it is too wide for grasping and hence attempts to pick up the box by one edge instead. The horn (Figure 19, Object 25) and the remote control (Figure 19, Object 33) are examples of objects that do not have perfectly parallel edges. However, they are still graspable due to the increasing threshold logic of the algorithm. The triangle (Figure 19, Object 36) is grasped by projecting one edge to the other side of the object perimeter, which happens to be a corner. With one edge and one corner in contact, the robot is able to grasp it. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Object** & **Success Rate (\%)** & **Object** & **Success Rate (\%)** \\ \hline Mouse & 100 & Kitchen knife & 100 \\ Duct tape & 100 & Marker & 100 \\ Canned drink & 100 & Toy shark & 100 \\ Stapler & 100 & Scissors & 100 \\ Mug & 100 & Rubber duck & 100 \\ Slinky toy & 90 & Keys & 90 \\ Stuffed toy & 100 & Screwdriver & 100 \\ Wire & 90 & Bowl & 100 \\ Sponge & 100 & Jar & 90 \\ Clothes peg & 100 & Circuit board & 100 \\ Toy Gun & 90 & Flashlight & 100 \\ Battery pack & 100 & Banana & 100 \\ Horn & 100 & Whistle & 100 \\ Spatula & 100 & Wine opener & 100 \\ Translucent box & 100 & Toothbrush & 100 \\ Swiss army knife & 100 & Spoon & 90 \\ Remote Control & 100 & Game console & 100 \\ Die & 100 & Triangle & 100 \\ Glasses & 90 & Ball & 100 \\ Electrical component & 100 & Clip & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Grasp success rate of each object for 10 trials each. Small, stick-like objects that have only one straight edge detected, like the screwdriver (Figure 19, Object 14) and the toothbrush (Figure 19, Object 30), are grasped along this edge. For other odd-shaped objects like scissors (Figure 19, Object 8) and glasses (Figure 19, Object 37), the algorithm is also able to identify a straight edge to grasp at. The algorithm is unable to identify any straight edges on objects like the mug (Figure 19, Object 9), the rubber duck (Figure 19, Object 10), and the ball (Figure 19, Object 38).