ABSTRACT
Building machines with humanlike form is not only an interesting scientific challenge, but also a practical engineering endeavor. With a physical form similar to humans, the humanoid robots are potential tools to be used as proxies or assistants of humans in performing tasks in the real world environments which are including rough terrain, steep stairs, and obstacles. Humanoid robots have recently evolved into an active research area with the creation of several humanoid robot systems and many related issues such as stability criterion, actual robot design and application, and dynamics analysis have been studied [1-4]. Among these issues a stable and reliable biped walking is the most fundamental and yet fully unsolved. So the quest for human-like walking robots is a main scientific goal of the artificial intelligence community [5-7]. Recently a few attempts have been made to develop human-like walking as modeling desired zero moment point (ZMP) trajectory [8] which poses an important criterion for the balance of the walking robots. In this study, a support vector machines (SVM) [9,10] is applied to model a humanoid walking robot. Support vector machines and kernel methods (KMs) have become in the last few years one of the most popular approaches to learning from examples with many potential applications in science and engineering. The basic theory is well understood and applications work successfully in practice. In many applications, SVM has been shown to provide higher performance than traditional learning machines and has been introduced as powerful tools for solving classification problems.
