## 1 Introduction  
A telepresence robot empowers a distant commanding user to experience real-time presence in remote locations [1]. Cameras are mounted on a robot to recognize the remote environment. A telepresence robot is similar to a mobile robotic system that enables remote users to experience physical mobility. Telemedicine, tele-doctor, telesurgery, and tele-habilitation can save time and money in various tasks. The framework for controlling a telepresence robot combines two-way communication, control signals, and video data, as shown in Figure 1. The telepresence robot operates effectively through signals received from the teleoperator. However, in the event of communication delays, the behavior of the telepresence robot becomes erratic and unpredictable in unfamiliar remote healthcare environments, potentially resulting in collisions and causing harm to individuals. During the period of delayed communication, it may be advantageous to implement an autonomous strategy that effectively assists the telepresence robot. This strategy would involve learning from the teleoperator's operating behavior and adapting to maneuver efficiently in the remote environment. Simultaneous localization and mapping (SLAM) were one of the compelling algorithms used in mobile robot navigation. The SLAM algorithm uses the input data from different sensors, such as the camera, Lidar, and other sensors, to reconstruct an offline map to understand the surrounding unknown environment [2]. A generalized algorithm flow of a telepresence robot is shown in Figure 2.  
The primary issue with the SLAM was using multiple robot sensors to calculate the input data and achieve the estimated environment information to devise the navigation-controlling signal. Visual SLAM [3] navigates by using images as the primary source, but it contains enormous and unnecessary data from the unknown environment and can better recognize the environment. The conventional visual SLAM estimates the camera pose, processes it, and then reconstructs the map. Achieving inter-frame estimations through matching between feature points by extracting image features improves visual SLAM, which has many limitations. Many challenges exist regarding the lack of texture in a single environment and target motion. Simultaneously, artificial intelligence has introduced the famous direction in respective fields of robot autonomous controlling with deep reinforcement learning (DRL) [4]. The decision-making capability and deep learning control the agent's actions. The main idea of deep learning (DL) is similar to a human neural network, which is to merge low-level features to form abstract to distinguish high-level representations through multilayered network structures. The primary purpose of reinforcement learning (RL) is to understand the optimum strategy for achieving the objective by increasing the agent's accumulative reward value acquired from the environment. Hence, RL's method focuses on learning approaches to resolve complications. RL is concerned about an agent that discovers how to execute a specific task through trial and error while interacting with unknown environment parameters, which provides feedback regarding reward at the end, as shown in Figure 3. The interaction of the agent and the environment is a continual process. Initially, at time \(t\), an agent receives information  
Figure 1: Generalized telepresence robot framework. Figure 2: Generalized algorithm flow of a remote-controlled telepresence robot. about its environment from its sensor's input \(s_{t}\), based upon which the agent selects an action \(a_{t}\) from the set \(A\) of the possible actions, then action \(a_{t}\) is executed in the environment [5]. The action \(a_{t}\) performed in the environment according to the state value \(s_{t}\), and its effect that the agent receives a scalar reward \(r_{t+1}\) and a new state \(s_{t+1}\). The main target of the agent is to maximize the reward it receives from the environment after more trials.