## 2 Related Work
### Crowd Navigation Using Reinforcement Learning
[44] used St-graphs to anticipate human actions, such as drinking, in a series of human-object interactions. [PERSON] et al. [45] proposed the use of st-graphs for modeling crowd behavior without any robotic interactions. The authors consider the influence of each pedestrian over all the pedestrians both in the vicinity and far away. Similarly, [PERSON] et al. [43] used the same St-graphs approach to train a model, which is then used by a robot to anticipate the crowd movement and navigate the crowd safely. St-graph has factors that observe node and edgefeatures at each time step and perform some computation on these features [44]. Each factor is represented by a recurrent neural network (RNN). The structure of the St-graph is modeled in such a way that the combined RNN network (node and edge RNNs) captures the relationship between spatial and temporal features to predict the optimum velocity the robot should take to reach the goal. For crowd navigation, the spatial features are the current locations of each pedestrian/human, and the temporal feature is the robot's velocity. This technique outperforms deep V-learning in success rate, defined as the percentage of the number of times the robot successfully reaches the destination. Furthermore, the authors note that this method scales well with an increasing number of humans. However, this method does not take into account static obstacles and does not consider any human-robot interaction requiring physical contact. All of the aforementioned RL techniques focus on mobile robots without direct physical contact with a human. However, being close to the patient and supporting the patient are crucial aspects of a walking assistant robot. In addition to that, all of the RL techniques only limit their human interactions by training their RL algorithms not to cross the person's boundary radius. Simply not crossing the person's boundary is not enough; the robot should also adhere to social norms while passing by people. To address this issue, [PERSON] et al. [46] focus on specifying explicit limits on interpersonal distance by conducting numerous social experiments where a robot approaches humans. The humans were given a survey to fill up to express their comfort/discomfort levels. The authors conclude in their research that anywhere between 0.0 and 0.45 m is a personal boundary between the humans, and the robot can pass anywhere between 1.2 and 3.6 m from the humans without raising their discomfort levels. Furthermore, the authors also state that a slow-approaching robot has a speed of 0.4 m/s and a fast-approaching robot has 1 m/s. From this paper, it can be concluded when the robot navigates the crowd, the robot's speed should be low while passing by a human in the crowd. The robots discussed in this section focus on a single task from a set of multiple characteristics that an autonomous walking assistant robot should possess. For example, some robots focus on gait tracking and static obstacle tracking, and some on crowd navigation. For crowd navigation, the mobile robots do not include any physical contact with a human guiding them in the crowd. However, both gait tracking and crowd navigation are essential for an RWA that could be used in hospitals. The robot walking assistant should always support the patient while walking and, at the same time, avoid static obstacles (e.g., sofas, beds, chairs, etc.) and also dynamic obstacles (e.g., crowds) to be able to work safely in hospitals. To the best of our knowledge, the presented work addresses the gap between robotic crowd navigation and robotic walking assistance. The next section presents the problem statement and the proposed architecture that encompasses all the essential characteristics of an RWA.