Rapid learning in robotics

Robotics deals with the control of actuators using various types of sensors and control schemes. The availability of precise sensorimotor mappings – able to transform between various involved motor, joint, sensor, and physical spaces – is a crucial issue. These mappings are often highly non-linear and sometimes hard to derive analytically. Consequently, there is a strong need for rapid learning algorithms which take into account that the acquisition of training data is often a costly operation. The present book discusses many of the issues that are important to make learning approaches in robotics more feasible. Basis for the major part of the discussion is a new learning algorithm, the Parameterized Self-Organizing Maps, that is derived from a model of neural self-organization. A key feature of the new method is the rapid construction of even highly non-linear variable relations from rather modestly-sized training data sets by exploiting topology information that is not utilized in more traditional approaches. In addition, the author shows how this approach can be used in a modular fashion, leading to a learning architecture for the acquisition of basic skills during an " investment learning " phase, and, subsequently, for their rapid combination to adapt to new situational contexts. ii Foreword The rapid and apparently effortless adaptation of their movements to a broad spectrum of conditions distinguishes both humans and animals in an important way even from nowadays most sophisticated robots. Algorithms for rapid learning will, therefore, become an important prerequisite for future robots to achieve a more intelligent coordination of their movements that is closer to the impressive level of biological performance. The present book discusses many of the issues that are important to make learning approaches in robotics more feasible. A new learning algorithm , the Parameterized Self-Organizing Maps, is derived from a model of neural self-organization. It has a number of benefits that make it particularly suited for applications in the field of robotics. A key feature of the new method is the rapid construction of even highly non-linear variable relations from rather modestly-sized training data sets by exploiting topology information that is unused in the more traditional approaches. In addition, the author shows how this approach can be used in a modular fashion, leading to a learning architecture for the acquisition of basic skills during an " investment learning " phase, and, subsequently, for their rapid combination to adapt to new situational contexts. The …