**1. Introduction**

Social Robotics is commonly defined as the research field dedicated to the socially skillful robots [1]. The main ability of social robots is to establish a natural interaction with humans. The Human-Robot Interaction (HRI) field of study tries to shape the interactions between one or more humans and one or more robots. Over the latest several years, there is an increasing interest in HRI due to the increasing usage of robots not only in industrial fields, but also in other areas as schools [2], homes [3], hospitals [4], and rehabilitation centers [5].

Consequently, in the near future, robots will concretely share environments with human beings to actively collaborate with them in specific daily tasks. The presence of a robot, in fact, could be a useful support during the management of daily activities [6,7], the promotion of social inclusion [8,9], and the suggestion of healthy activities [10,11]. Particularly, recent literature findings underline that robots could help users in their daily life by bringing them objects that they need ( i.e., a bottle of water, a specific drug ) [12], which helps them in dressing tasks [13,14] or in getting in contact with their families or authorities in dangerous situations [9]. An easy and continuous connection with other people (i.e., relatives, friends, or doctors), could promote social inclusion of people with disabilities or elderly people and increase the quality of their life [15]. Therefore, in this context, there is a growing necessity for developing behavioral models for social robots to have a high quality interaction and level of acceptability in providing useful and efficient services [16,17]. Remarkably, how people accept, perceive, interact, and cooperate with this intelligent machine in their life is still somewhat unknown. However, researchers with different backgrounds are trying to meet this challenge [18].

First, to achieve fluent and effective human-like communication, robots must seamlessly integrate the necessary social behaviors for a given situation using a large number of patterned behaviors that people employ to achieve particular communicative goals. Furthermore, robots should be endowed with the capability to understand feelings, intentions, and beliefs of the user, which are not only directly expressed by the user, but that are also shaped by bodily cues (i.e., gaze, posture, facial expressions) and vocal cues (i.e., vocal tones and expressions) [19]. The non-verbal immediacy, which characterizes communications between humans, should be conveyed in Human-Robot Interaction (HRI). Moreover, the ability to replicate human non-verbal immediacy in artificial agents is twofold. On one side, it allows the detection of emotional and cognitive state of the user, which is useful to develop proactive robots. On the other side, it allows us to shape the behavior of the robot in order to encode behavior capabilities in the interaction as those of humans. The latter case leads to the possibility to automatically generate new robotic behaviors that the robot learns directly from the user.

The first attempts to solve this challenge have been performed by developing intelligent systems able to detect user's emotions [20] and by identifying the key factors that should be adjusted to make the interaction smoother (i.e., interpersonal distance, mental state, user's feedback, and user's profile) [21]. More advanced steps should be performed so that robots are endowed with cognitive and affective capabilities that could provide them with tools to establish empathetic relationships with users and to gain social cognitive mechanisms that are necessary to be perceived as a teammate [22,23].

Second, it is important to remark that the robot's ability to establish empathic relationships has a key role in HRI since it indicates the degree of perceived bodily and psychological closeness between people. Over the last several years, researchers put a lot of effort in understanding how psychology and cognitive neuroscience could be integrated in the design process of artificial cognitive architectures to achieve this target. The field of brain-inspired technologies has become a hot topic in the last several years.

In this context, this paper aims to analyze the current state of the art of behavioral models to find barriers and limitations to provide guidelines for future research studies in this area. Particularly, two databases (namely Scopus and Web of Science) were analyzed to retrieve papers linked with cognitive robotics architecture and model robot empathy, affordance, facial expression, cultural adaptation, and the social robot. In effect, this survey expresses this growing interest and the need to support the research studies in this field and to organize the large amount of work, which is loosely related to the topic underling the scientific challenges. The main issues of this area is related to the fact that several models are too often described from a theoretical point of view without being tested on a real robot and the ones that are tested on a real robot are often tried in a single environment with people belonging to a specific culture [24]. Specifically, researchers are working on the development of cognitive architectures approaching a fully cognitive state, embedding mechanisms of perception, adaptation, and motivation [25]. Particularly, from the analysis of the state of the art, the papers of this survey are grouped according to three main application areas: cognitive architectures, behavioral adaptation, and empathy.


to reproduce this ability in robotic agents to establish an empathetic connection with the user, which improves Human-Robot Interaction (HRI). Empathy is a sub-category of the behavioral adaptation. However, we decide to make separate categories to be aligned with some recent papers [17,30,31].

In this review, several models and architectures used in social robots are presented to evaluate how these attempts fare in achieving an efficient robot-human interaction. A comparison with works presenting experimentation to demonstrate the persuasiveness of robots is also provided to highlight limitations and future trends. In details, the paper is organized as follows: in Section 2, the research methodology for the review is explained. In Sections 3 and 4, the results and the discussions regarding the papers are shown. In Section 5, a summary of the review and its conclusions are presented.

## **2. Materials and Methods**

This section presents the methodology used in the paper to select the most appropriate recent developments as published in the literature, covering the topics of behavioral models for robots.

## *Study Selection Procedures*

This paper reviews empirical studies published between 2010 and 2018 since most of the advances in this area have occurred within that timeframe. A bibliography was developed upon research in Scopus and Web of Science electronic databases. Reference lists of included articles and significant review papers were examined to include other relevant studies. The search queries contained the following terms and were summarized in Table 1.


**Table 1.** List of keywords used in this review work.

Application of these search keys provided a total of 4916 hits with 1520 hits in Web of Science in the field "Topic" and 3396 hits in Scopus in "Article title, abstract, keywords" fields.

After deletion of duplicates, the titles and abstracts retrieved by the electronic search were read first, to identify articles deserving a full review. Papers about users' emotion recognition and about emotions as unique input for HRI were excluded. Additionally, papers not written in English were excluded. A total of 1297 works was selected at this stage.

Then, a full-text assessment was carried out. The reading process led to the exclusion of 1241 papers that were out of topic, papers focusing only on definitions and taxonomy, papers for missing the model's evaluation, and papers focusing more on users' perception about robot's abilities without behavioral adaptation.

The final list of papers includes 56 studies, which satisfy all the following selection criteria: (i) employment of cognitive architectures and/or behavioral models, (ii) explanation of cognitive

architectures and/or behavioral models, (iii) research focus on robotic agent's capabilities; (iv) behavioral adaptation according to different strategies, and (v) analysis conducted on social or assistive robots. The studies' selection process is shown in Figure 1.

**Figure 1.** Selection process of relevant papers.

#### **3. Results**
