This study focuses on biomimetic sensory motor control of a robotic arm. We have developed a command circuit that was mathematically deduced from physical and mathematical constraints describing the function of cerebellar pathways. The control circuit contains an internal predictive model of the direct biomechanical function of the limb placed in a closed loop, so that the circuit computes an approximate inverse function. The structure of the model resembles the anatomic connectivity of the cerebellar pathways. In this paper, we present an application of this model to the control of a 2-link robotic arm actuated by four single-joint McKibben muscles and report the results obtained by simulation and real-time learning of 2 degrees of freedom pointing movements.

In recent years, advances and improvements in engineering and robotics have been strengthening interactions between biological science and robotics in the goal of mimicking the complexity of biological systems. In this paper, motor control paradigms inspired by human mechanisms of sensory-motor coordination are applied to a biologically-inspired, purpose-designed robotic platform. The goal was to define and implement a multi-network architecture and to demonstrate that progressive learning of object grasping and manipulation can greatly increase performance of a robotic system in terms of adaptability, flexibility, growing competences and generalization, while preserving the robustness of traditional control. The paper presents the neural approach to sensory-motor coordination and shows preliminary results of the integration with the robotic system by means of simulation tests and experimental trials.

We introduce a hybrid intelligent karyotyping system based on two different types of artificial neural networks (ANNs) and chromosome's features obtained by digital image processing techniques. A microscope equipped with a CCTV camera and a microcomputer including a frame grabber are the basic components of our hardware set-up. The inputs to the ANN structure are obtained directly from digital chromosome images by using two recently developed object detection and object skeletonizing algorithms. Moreover, the band patterns of chromosomes are represented by applying wavelet transform techniques on the gray level profiles of chromosomes. The network parameters are determined by using the results of many training and testing experiments in order to reach an optimal state from the classification performance point of view.