# Singulation of Objects in Cluttered Environment Using Dynamic Estimation of Physical Properties
## 1 Introduction  
Robot grasping is well enough developed for a structured environment [1; 2]. However, interaction in unstructured environments is quite a tedious task for robots. Different techniques have been proposed for object manipulation in cluttered environments, such as data driven approaches [3] and using a hierarchy of supervisor for online learning from demonstration [4; 5]. In robot manipulation, the objects can be moved from initial position to goal position through quasi-static or dynamic manipulation. The object singulation can be achieved by either pushing (quasi-static manipulation method) or scattering (dynamic manipulation method) the objects. In this paper, the methodologies are developed for object singulation, considering the dynamic manipulations. Object singulation assists grasping objects in a cluttered environment by creating fewer obstacles and more clearance for the robot end effector. Especially, when objects are close to each other or come into contact, it is hard for the robot to grasp an individual object without separating it from other objects. That is why it is sometimes necessary to scatter objects by hitting them with a manipulator for a short period of time to facilitate a grasping operation. Such cluttered environments can be found in our living rooms, as shown in Figure 1a. The task will be identifying objects, grasping, and finallyrearranging them at their original locations. The singulation of objects will help in identification and grasping of objects. Another emerging issue is the autonomous assembly by a robot manipulator. Lego parts based assembly example is shown in Figure 1b. Many objects are mixed together, and the robot performs the object singulation and then should identify different parts in sequence in real-time, grasp it, and complete the assembly. Different methodologies of object singulation are employed by using a pushing motion in cluttered environment [6; 7; 8; 9; 10]. [PERSON] et al. [7] proposed a neural network-based approach to separate unknown objects in clutter through favorable push actions. By performing selective pushes, [PERSON] et al. [8] proposed a method for separating an unknown number of objects by disambiguating potential object boundaries. [PERSON] et al. [11] used a push-grasping technique to enhance the grasping capabilities of the robot by introducing the concept of capture region. Standard model-based approaches are also applied for object singulation [12; 13; 14] in a cluttered environment. The model-based approaches require knowledge of physical properties of the environment. Without any prior knowledge, the estimation of the physical properties is a challenging task. However, the physical properties can be estimated by processing the recorded video of dynamic interaction of objects along with the analytical model. [PERSON] et al. [15; 16] proposed a technique to acquire physical properties of objects from unlabeled videos. However, this technique is not directly applicable for an object singulation application. Impulse model-based methodologies are introduced for the robot and environment interaction applications [17; 18; 19]. By extending those methodologies, we propose an analytical model based scattering technique for object singulation in a controlled manner. Controlled scattering implies achieving a desired distance among objects after collision as shown in Figure 2. The robot performs scattering by hitting the object nearest to the gripper. As a result, the nearest object collides with the surrounding objects and makes it easy for the robot to pick up the target object. In this paper, an impulse-based model is used for analytical modeling of the environment. A robot manipulator will be used as a means of impulse given to the object. The problem of physical properties estimation is overcome by using a vision-based technique. A virtual world simulator is developed to perform a controlled scattering without disturbing the real-world environment. In Section 3, a generalized impulse model is introduced.