**1. Introduction**

146 The Future of Humanoid Robots – Research and Applications

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> Development of humanoid robots has to address two vital aspects, namely physical appearance and gestures, that will allow the machines to closely resemble humans. Other aspects such as "social" and "emotional" will enable human-machine interaction to be as natural as possible. The field of robotics has long been investigating how effective interaction between humans and autonomous and intelligent mechanical system can be possible (Goodrich & Schultz., 2007). Several distinctive features have been determined depending on whether a robot that acts as an assistant (for example, in the course of a business) or as a companion is required. In the case of humanoid robots, the human appearance and behavior may be very closely linked and integrated if you adopt a cognitive architecture that can take advantage of the natural mechanisms for exchange of information with a human. The robot that cooperates in the execution of an activity would benefit from the execution of its tasks if it had a mechanism that is capable of recognizing and understanding human activity and intention (Kelley et al., 2010), with perhaps the possibility of developing imitation learning by observation mechanisms.

> On the other hand, if we consider the robot as a partner, then it plays an important role in sharing the emotional aspects: it is not essential to equip the robot with emotions, but it is important that it can "detect" human emotional states (Malatesta et al. 2009).

> The cognitive architectures allow software to deal with problems that require contributions from both the cognitive sciences and robotics, in order to achieve social behavior typical of the human being, which would otherwise be difficult to integrate into traditional systems of artificial intelligence. Several cognitive models of the human mind can find common ground and experimental validation using humanoid agents. For example, if we approach the study of actions and social interactions involving "embodied" agents, the concept of motor resonance investigated in humans may play an important role (Chaminade & Cheng, 2009) to achieve sophisticated, yet simple to implement, imitative behaviors, learning by demonstration, and understanding of the real scene.

> In recent years, there is often talk of mirror neurons, which are evidence of the physiological motor resonance at the cellular level with regard to action, action understanding and imitation. But the resonance is applicable in other contexts such as cognitive emotions, the sensations of physical pain, and in various components of the actions of agents interacting socially (Barakova & Lourens, 2009; Fogassi, 2011).

> Cognitive models proposed would make the humanoid robot capable of overcoming the socalled "Uncanny Valley of eeriness" (Saygin et al., 2011), by allowing the humanoid is

Affective Human-Humanoid Interaction Through Cognitive Architecture 149

thought of as a representation of emotional states that, in addition to influencing behavior, also helps to manage the detection and recognition of human emotions. Similarly, human intentions may somehow be linked to the expectations and beliefs of the intelligence system. In a wider perspective, the mental capabilities (Vernon et al. 2007) of artificial computational agents can be introduced directly into a cognitive architecture or emerge from the interaction of its components. The approaches presented in the literature are numerous, and range from cognitive testing of theoretical models of the human mind, to robotic architectures based on perceptual-motor components and purely reactive behaviors (see

Currently, cognitive architectures have had little impact on real-world applications, and a limited influence in robotics, and the humanoid. The aim and long-term goal is the detailed definition of the Artificial General Intelligence (AGI) (Goertzel, 2007), i.e. the construction of artificial systems that have a skill level equal to that of humans in generic scenarios, or greater than that of the human in certain fields. To understand the potential of existing cognitive architectures and indicate their limits, you must first begin to classify the various proposals presented in the literature. For this purpose, it is useful a taxonomy of cognitive architectures (Vernon et al. 2007; Chong et al., 2007) that identifies three main classes, for example obtained by characteristics such as memory and learning (Duch et al., 2008) . In this classification are distinguished symbolic architectures, emerging architectures, and hybrid architectures. In the following, only some architectures are discussed and briefly described, indicating some significant issues that may affect humanoids, and affective-based interactions. At present, there are no cognitive architectures that are strongly oriented to the implementation of embodied social agents, nor even were coded mechanisms to emulate the so-called social intelligence. The representation of the other, the determination of the self, including intentions, desires, emotional states, and social interactions, have not yet had the necessary consideration and have not been investigated approaches that consider them in a

The symbolic architectures (referring to a cognitivist approach) are based on an analytical approach of high-level symbols or declarative knowledge. SOAR (State, Operator And Result) is a classic example of an expert rule-based cognitive architecture (Laird et al., 1987). The classic version of SOAR is based on a single long-term memory (storing productionrules), and a single short-term memory (with a symbolic graph structure). In an extended version of the architecture (Laird 2008), in addition to changes on short and long-term memories, was added a module that implements a specific appraisal theory. The intensity of individual appraisals (express either as categorical or numeric values) becomes the intrinsic rewards for reinforcement learning, which significantly speeds learning. (Marinier et al., 2009) presents a unified computational model that combines an abstract cognitive theory of behavior control (PEACTIDM) and a detailed theory of emotion (based on an appraisal theory), integrated in the SOAR cognitive architecture. Existing models that integrate emotion and cognition generally do not fully specify why cognition needs emotion and conversely why emotion needs cognition. Looking ahead, we aim to explore how emotion can be used productively with long-term memory, decision making module, and

The interaction is a very important aspect that makes possible a direct exchange of information, and may be relevant both for learning to perform intelligent actions. For

1Biologically Inspired Cognitive Architectures Society -Toward a Comparative Repository of Cognitive

Architectures, Models, Tasks and Data. http://bicasociety.org/cogarch/

Comparative Table of Cognitive Architectures1).

unified manner.

interactions.

perceived by human beings not as artificial machine but as a credible social interacting entity. In this sense, the recent experimental data have confirmed the importance of "natural" movements (Saygin et al., 2011) that are expected from the observation of a robot with human features, even if it is a process yet to fully understand, that continually generate predictions about the environment, and compares them with internal states and models. Mirror neurons are assumed to be the neural basis for understanding the goals and intentions (Fogassi 2011), allowing for the prediction of the actions of the individual who is observed, and its intentions. Various studies indicate that they are also involved in the system of empathy, and emotional contagion (Hatfield et al. 1994), explaining that the human tendency to automatically mimic and synchronize facial expressions, vocalizations, postures, and movements with those of another person.

The classical approach in robotics based on the perception-reasoning-action loop has evolved towards models that unify perception and action, such as the various cognitive theories arising from the Theory of Event Coding (Hommel et al., 2001). Similarly, the objectives are integrated with the intentions, and emotions with reasoning and planning.

An approach that considers the human-robot interaction based on affective computing and cognitive architectures, can address the analysis and reproduction of social processes (and not only) that normally occur between humans, so that a social structure can be created which includes the active presence of a humanoid. Just as a person or group influences the emotions and the behavior of another person or group (Barsade, 2002), the humanoid could play a similar role in owning their own emotional states and behavioral attitudes, and by understanding the affective states to humans to be in "resonance" with them.

The purpose of this chapter is to consider the two aspects, intentions and emotions, simultaneously: discussing and proposing solutions based on cognitive architectures (such as in Infantino et al., 2008) and comparing them against recent literature including areas such as conversational agents (Cerezo et al., 2008).

The structure of the chapter is as follows: firstly, an introduction on the objectives and purposes of the cognitive architectures in robotics will be presented; then a second part on the state of the art methods of detection and recognition of human actions, highlighting those more suitably integrated into an architecture cognitive; a third part on detecting and understanding emotions, and a general overview of effective computing issues; and finally the last part presents an example of architecture that extends on the one presented in (Infantino et al., 2008), and a discussion about possible future developments.
