# Direction-Based Hybrid Strategy Combining Pushing and Hitting for Fast Object Singulation
## 7 Discussion  
In this study, we proposed the initial direction-based hybrid singulation approach. In the start, we used hitting to scatter the objects to achieve the singulation of disks in clutter. All of the disks were not singulated using just a single hit, as a result, we needed to use pushing to completely separate all of the objects. Previous studies have used pushing to singulate objects but they have not developed a physics-based analytical model to find the appropriate direction of pushing or hitting. In our study, our focus was to reduce the overall time taken in order to singulate objects effectively. Several studies based on the impulse between the robot and the environment have been discussed [6,11]. However, dynamic and quasi-static approaches alone cannot achieve singulation for various cases. [PERSON] et al. studied a non-prehensile manipulation approach in clutter using a human [12]. However, this approach needs an input from a human which consumes time. In this approach, they have used pushing as a non-prehensile manipulation method to manipulate objects in clutter. In some studies, there is the problem of approaching invisible spaces which creates a limitation for the system to singulate objects in clutter [13]. However, this problem did not arise in this study and all of the disks were visible in the workspace and there were no such invisible areas. Several studies have faced the problem of the unnecessary displacement of objects [3]. The main reason for this problem is the randomized hitting or pushing that cause the unnecessary displacement of objects. Our approach, based on the initial direction for hitting and pushing, solved this problem because it helped the disks to remain in the workspace when relying on a hybrid approach of hitting and pushing. In this paper, the main focus was to perform a hybrid singulation using the initial direction based on our mathematical model and the emphasis was on acquiring the desired singulation among circular disks in less time. In the future, we would like to extend this study to singulate objects of various sizes and shapes. Although this paper has focused on an analytical way of object singulation in clutter, a learning-based approach has many benefits. Reinforcement learning-based pushing for obtaining optimal pushing policies is another way proposed to singulate objects in clutter. However, this approach lacks pushing in more directions [14]. In various scenarios where it is hard to find grasps, pushing or sweeping actions can be used for automated bin picking to singulate objects. [PERSON] et al. [15] proposed a novel neural network-based approach that separates unknown objects in clutter by selecting favorable push actions. [PERSON] et al. [16]  
Figure 14: Comparison among our proposed direction-based hybrid singulation approach and other approaches. considered the singulation of objects by alternating pushing and grasping. Here, pushing was used as a test action to evaluate whether an isolated set of cloud points is a single object or not. If the set of cloud points splits into multiple units after pushing, it repeats the pushing for these smaller units. If not, the robot tries to grasp the object (represented as a set of cloud points). A heuristic for choosing the direction and length of pushing was given purely based on the geometry of the cloud, without considering the dynamic property of the object. [PERSON] et al. [17] compared five pushing policies based on object separation and grasp confidence, which is probability of grasp success. For each pushing strategy, the degree of object separation and grasp confidence was computed and it is shown that the degree of object separation is not necessarily correlated with grasp success. [PERSON] et al. [14] used a learning-based method to singulate objects. They did not explicitly consider the geometry of the object and also use a limited number of pushing directions, which can increase the number of pushing.