## 1 Introduction  
Within the educational context, robots can take the roles of learning tools, learning companions, teaching assistants and mediators to support group learning [1; 2; 3]. For language learning activities that involve pronunciation and spelling knowledge, the use of robots has been proposed [2]. In this regard, a system integrated by automatic speech recognition (for assessment of spoken words) and speech-to-text transcription (for the presentation of correct spelling) can provide support for language learning. The inclusion of a robotic system (particularly a humanoid robot) within the learning task can improve the user experience and increase the learning rate. As mentioned in [4], in comparison with screen-based agents, robotic partners have been shown to improve users' compliance with tasks [5], maintain more effective long-term relationships [6] and improve learning gains when acting as tutors [7]. Particularly, robots that resemble people (humanoid robots) motivate curiosity and are more engaging [2]: children treat robots that look and move like people as real speakers/listeners. Furthermore, the young students do not have to worry about the consequences (e.g., verbal punishment) of making pronunciation or syntax errors when interacting with the robot. This may lead to reduce students' anxiety and improve the willingness to participate in the learning activity [2]. Handwriting is one of the main activities considered in language learning activities for children, because it provides the visual representation of spoken words. As discussed in [8], although there has been a relatively low status and lack of attention given to handwriting at the primary and secondary school levels, research has determined that handwriting in the form of automatic letter writing facilitates the higher order composing processes of text production. Hence, difficulties in handwriting skills at an early age often negatively affect academic performance and self-esteem [4; 9; 10]. In the technological context, handwriting is one of the most complex tasks performed by robots, because it requires fine control of servomechanisms. This control involves the dynamic aspects of the writing/drawing task, such as speed, force and position [11]. Human-like factors, as fatigue, are also explored in the control of robot arms for handwriting applications [12]. The accomplishment of the handwriting task has many important implications in other fields of human-robot interaction (HRI), such as teaching/transfer of handwriting skills [13; 14; 15], analysis and replication of human motion for calligraphy education [16], education and training of forensic handwriting experts [17], application in rehabilitation therapy [17] and communication support for people with neuromuscular disorders via a brain-machine interface (BMI) [18]. Some examples of robots used for handwriting tasks (calligraphy) of Chinese characters are presented in the literature. The robot presented in [19] was developed with advanced controllers (Mitsubishi Q-02 and Q-172-CPU for logic and drive control), servo-amplifiers (Mitsubishi MR-J2S-20B) and servo-motors (HC-KFS23). The use of a standard seven DOF KUKA light weight robot for the calligraphy task was presented in [20]. In [21], dynamic and kinematic models were developed for the calligraphy of Japanese characters. These models were tested with a simulated three DOF SCARA robot. Handwriting of the Latin alphabet is commonly performed because it is the basic alphabet for many languages around the world. Some examples of robots performing writing of words with the Latin alphabet are presented in the literature with the SCARA system [22], the NAO system [23], and a CNC GFY 44/48 system [17]. Particularly, the system presented in [17] is able to reproduce human-written characters (signatures) enabling its use for forensic purposes. Research relevant to modeling (kinematics) and control for this task can be found in [11; 12; 17; 24].