A new and efficient kinematic position and velocity solution scheme for spatial hyper-redundant manipulators is presented. The manipulator's arm has discrete links and universal joints. Backbone curve concepts and a modal approach are used to resolve the manipulator's redundancy. The effects of the mode shapes and the slope of the backbone curve at the starting point on the workspace are studied. It is shown that the usage of conventional mode shapes limits the workspace of the hyper-redundant arm. By introducing new mode shapes, an improved workspace is obtained. A simple and efficient recursive fitting method is introduced to avoid complications involved with solving systems of nonlinear algebraic equations. This method also guarantees the existence of solutions for the inverse kinematic problem at the velocity level. Velocity properties of the backbone curve are investigated and the inverse velocity propagation is solved for the spatial hyper-redundant arm. The velocity propagation scheme is recursive and is efficiently applicable to any number of links.

Biasing, once regarded as "cheating" in the machine learning community, is now understood and accepted as a necessary part of learning. However, despite its wide acceptance and recognition, biasing has never been studied as a separate research issue, except by Hailu & Sommer (1999), who made an attempt to shed light on the relationship between the quality of the bias and learning trials. So far, the general view held in biasing a learning system is to look for a bias that maximally collapses the search space. It is well-known, however, that reckless reduction of the search space often leads to sub-optimal learning. Regardless of the final level of optimality, this paper challenges this broadily accepted biasing scheme from the point of view of accelerating the learning process itself. Is a large search space a definitive indication of slow learning? We give a non-affirmative answer to this dogma by presenting a typical robot learning scenario. Experiments clearly indicate that, in spite of its large search space, a bias that is derived from the unique characteristics of the task shows better learning behavior than a bias that reduces the search space aggressively.