Abstract
This paper presents a multi-sensor humanoid robotic head for human robot interaction. The design of the robotic head, Muecas, is based on ongoing research on the mechanisms of perception and imitation of human expressions and emotions. These mechanisms allow direct interaction between the robot and its human companion through the different natural language modalities: speech, body language and facial expressions. The robotic head has 12 degrees of freedom, in a human-like configuration, including eyes, eyebrows, mouth and neck, and has been designed and built entirely by IADeX (Engineering, Automation and Design of Extremadura) and RoboLab. A detailed description of its kinematics is provided along with the design of the most complex controllers. Muecas can be directly controlled by FACS (Facial Action Coding System), the de facto standard for facial expression recognition and synthesis. This feature facilitates its use by third party platforms and encourages the development of imitation and of goal-based systems. Imitation systems learn from the user, while goal-based ones use planning techniques to drive the user towards a final desired state. To show the flexibility and reliability of the robotic head, the paper presents a software architecture that is able to detect, recognize, classify and generate facial expressions in real time using FACS. This system has been implemented using the robotics framework, RoboComp, which provides hardware-independent access to the sensors in the head. Finally, the paper presents experimental results showing the real-time functioning of the whole system, including recognition and imitation of human facial expressions.
Highlights
The creation and design of social robots has drawn attention to many fields of science
The emotional states of the user and how the robot is able to estimate them are one of the most critical processes in natural human-robot interaction (HRI). Knowing and understanding these human emotions helps social robots to adapt their communication in real time, improving and enriching the interaction [3]. This kind of HRI is usually known as affective HRI, which has become a central part of the social robotics field in recent years
A set of tests evaluate the performance of the facial expression recognition system through the Candide-3 reconstruction model
Summary
The creation and design of social robots has drawn attention to many fields of science. Interesting studies that combine psychological and robotics theories have demonstrated that the mechanical design of the platform and the social skills with which the robot is programmed would increase the empathy and the acceptance level of the robot [1,2] In this respect, the emotional states of the user and how the robot is able to estimate them are one of the most critical processes in natural human-robot interaction (HRI). Knowing and understanding these human emotions helps social robots to adapt their communication in real time, improving and enriching the interaction [3] This kind of HRI is usually known as affective HRI, which has become a central part of the social robotics field in recent years
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