The purpose of this research is to develop multitalented humanoid robots, based on technologies featuring high-computing and control abilities, to perform onstage. It has been a worldwide trend in the last decade to apply robot technologies in theatrical performance. The more robot performers resemble human beings, the easier it becomes for the emotions of audiences to be bonded with robotic performances. Although all kinds of robots can be theatrical performers based on programs, humanoid robots are more advantageous for playing a wider range of characters because of their resemblance to human beings. Thus, developing theatrical humanoid robots is becoming very important in the field of the robot theatre. However, theatrical humanoid robots need to possess the same versatile abilities as their human counterparts, instead of merely posing or performing motion demonstrations onstage, otherwise audiences will easily become bored. The four theatrical robots developed for this research have successfully performed in a public performance and participated in five programs. All of them were approved by most audiences.

This paper presents the creation of a robot capable of real-time drawing artistic portraits and signature. The application is based on the successful integration of multidisciplinary techniques including face detection, image processing, face image and characters segmentation, space coordinates transformation, trajectory planning, and robot arm motion control. The autonomous human face portrait is conducted by on a two-armed and twin-wheeled robot with a CCD camera on the head. A large number of real-time portrait experiments show that when the face of the people is properly illuminated, the robot can consistently use its 6-DOF arm to draw a face portrait consisting of smooth line segments and sign its name on the portrait.

This paper presents a cognitive learning system for robot recognition and composite action learning. The cognitive system of the robot is an artificial neural network trained to recognize and handle objects through imitation and backpropagation algorithm learning. The robot is first trained to learn the representation of action words, object categories and grounded language understanding. Following a human tutor's linguistic instructions, the robot autonomously transfers the grounding form directly basics knowledge to new higher level composite knowledge.

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