**2. Related research**

230 The Future of Humanoid Robots – Research and Applications

needs to take care of large number of data to recognize speech. Human speech contains three types of information: who the speaker is, what the speaker said, and how the speaker said it [Fong, 2003]. Depending on what information the robot requires, it may need to perform speaker tracking, dialogue management or even emotion analysis. Most systems are also sensitive to mis-recognition due to the environmental noise. On the other hand, gestures are expressive, meaningful body motions such as physical movements of head, face, fingers, hands or body with the intention to convey information or interact with the environment. Hand and face poses are more rigid, though its also varies little from person to person. However, humans will feel more comfortable in pointing at an object than in verbally describing its exact location. Gestures are an easy way to give geometrical information to the robot. However, gestures are varying among individuals or varying from instance to instance for a given individual. The hand shape and human skin-color are different for different persons. The gesture meanings are also different in different cultures. In human-human communications, human can adapt or learn new gestures or new users using own intelligence and contextual information. Human can also change each other behaviours based on conversation or situation. Achieving natural gesturebased interaction between human and robots, the system should be adaptable to new users, gestures and robot behaviors. This chapter includes the issues regarding new users, poses, gestures and behaviours recognition and adaptation for implementing human-

Adaptivity is the biological property in all creatures to survive in the biological world. It is the capability of self-modification that some agents have, which allows them to maintain a level of performance in front of environmental changes, or to improve it when confronted repeatedly with the same situation [Torras, 1995]. Gesture-based human-robot natural interaction system could be designed so that it can understand different users, their gestures, meaning of the gestures and the robot behaviours. Torras proposed robot adaptivity technique using neural learning algorithm. This method is computationally inexpensive and there is no way to encode prior knowledge about the environment to gain the efficiency. It is essential for the system to cope with the different users. A new user should be included using on-line registration process. When a user is included the user may wants to perform new gesture that is ever been used by other persons or himself/herself. In that case, the system should include the new hand

In the proposed method, a frame-based knowledge model is defined for gesture interpretation and human-robot interaction. In this knowledge model, necessary frames are defined for the known users, robots, poses, gestures and robot behaviours. The system first detects a human face using a combination of template-based and feature-invariant pattern matching approaches and identifies the user using the eigenface method [Hasanuzzaman 2007]. Then, using the skin-color information of the identified user three larger skin-like regions are segmented from the YIQ color spaces, after that face and hand poses are classified by the PCA method. The system is capable of recognizing static gestures comprised of face and hand poses. It is implemented using the frame-based Software Platform for Agent and Knowledge Management (SPAK) [Ampornaramveth, 2001]. Known gestures are defined as frames in SPAK knowledge base using the combination of face and hand pose frames. If the required combination of the pose components is found then corresponding gesture frame will be activated. The system learns new users, new poses using multi-clustering approach and combines computer vision and knowledge-based approaches in order to adapt to different users, different gestures and robot behaviours.

robot interaction in real-time.

poses or gestures with minimum user interaction.

In this chapter we have described a vision and knowledge-based user and gesture recognition as well as adaptation system for human–robot interaction. Following subsections summarize the related works.
