## 4 Experiments and Results
### Robotic Gripper Teleoperation Execution  
The dual anthropomorphic grippers share similar specifications. They only differ in size and trivial structural connections. In this experiment, we first used the smaller gripper to demonstrate the teleoperation with sign-language. Then, we used the bigger gripper to test the teleoperation for grasping tasks. Both experiments were implemented on a PC platform with Python development environment. The ROBOTIS Dynamixel SDK and LeapSDK were used as the software development kit for the Dynamixel motor MX-64R and Leap Motion Controller, respectively. First, a wide range of common hand gestures were tested in the teleoperation of the smaller gripper with sign-language demonstrations. The position control strategy was adopted for the motor control in this setup. The illustration of the human hand gestures and corresponding sign-languages of the robotic gripper is shown in Figure 8.  
In Figure 8, it is observed that the sign-language gestures of the gripper are properly matched with the corresponding human hand gestures. As an excellent somatosensory controller, the Leap Motion Controller provides a potential markerless and contactless human-robot interface. Then, we used the bigger gripper to test the teleoperation for grasping tasks. The torque control strategy was used in the motor control. Many common objects in our daily life were adopted in this experiment, with various shapes, hardnesses, sizes, and textures, ranging from the rigid bottle, fresh pepper, and slender fan, to deformable balloon and fragile egg. The grasping tests are illustrated in Figure 9. The characteristics of the grasped objects are listed in Table 1. The RRI value of the fingers during the teleoperation is given in Table 2.  
Figure 7: Joint values tracking in robotic arm teleoperation. Figure 8: Sign-language of the smaller gripper with human hand gestures and Leap Motion Controller. _Appl. Sci._**2020**, _10_, 2086 \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Item** & **Feature Size** & **Weight** & **Shape** & **Category** & **Hardness** \\ \hline Apple & 70 mm \(\times\) 84 mm & 223.3 g & irregular & fruit & hard \\ Toy ball & 156 mm & 107.0 g & spherical & plastic & soft \\ Fan & 62 mm \(\times\) 29 mm \(\times\) 12 mm & 30.5 g & cuboid & wooden & hard \\ Pineapple & 232 mm \(\times\) 116 mm & 873 g & irregular & fruit & hard \\ Pepper & 98 mm \(\times\) 72 mm & 262.6 g & irregular & vegetable & hard \\ Egg & 56 mm \(\times\) 44 mm & 58.7 g & irregular & crystals & hard \\ Cup & 266 mm \(\times\) 78 mm & 316.6 g & cylindrical & metal & hard \\ Spoon & 68 mm \(\times\) 16 mm & 2.1 g & cuboid & plastic & hard \\ Umbrella & 257 mm \(\times\) 58 mm & 353.5 g & cylindrical & fabric & hard \\ Drinking water & 297 mm \(\times\) 88 mm & 1533 g & cylindrical & plastic & soft \\ Balloon & 148 mm & 3.0 g & irregular & plastic & soft \\ Tennis & 67 mm & 57.5 g & spherical & fabric & hard \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristic of various objects used in the gripper teleoperation experiment. Figure 9: Teleoperation of the bigger gripper for grasping tasks with human hand gestures and Leap Motion Controller. It was observed that the teleoperation of the bigger gripper is able to handle a broad range of common objects for grasping tasks. It is well known that different objects correspond to different grasping modes. In view of individual characteristics of the grasped object, the operator can adjust his hand gestures to adapt to different grasping modes, which are further mapped onto the corresponding grippers. With the torque control mode of the Dynamixel motor, the gripper is enabled to grasp fragile and deformable objects. The grasping tightness and target position would be automatically adjusted with the object deformation, which is more flexible and robust with different objects grasping tasks, with different weights, shapes, sizes, hardnesses, and textures.