With the evolution of our complex technological society and the introduction of new notions and innovative theoretical tools such as cognitive and neural aspects of robotics, there have been some new evolutions of theoretical methodology. These evolving and innovative theoretical tools are providing some intelligence and robustness in robotic systems similar to what is found in natural biological species. Cognition and intelligence-the ability to learn, understand, and adapt-are the creation of

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... nature, and they play a key role in human actions as well as in many other biological species. Humans possess some robust attributes of learning and adaptation and that is what makes them so intelligent. Humans react through the process of learning and adaptation on information received through a widely distributed network of sensors and control mechanisms in our body. The faculty of cognition which is contained in our carbon-based computer-the brain-acquires information from the environment through various sensory mechanisms such as vision, hearing, touch, taste, and smell. Then the process of cognition-cognitive computing-integrates this information through its intricate neural networks and provides appropriate actions. The cognitive process then advances further toward some attributes such as learning, recollection, reasoning, and control. We are learning from the carbon-based cognitive computer-the brain-and are now in the process of inducing perception, cognition, and intelligence in robotics machines. One of our aims is to construct a robotic vehicle that can think and operate in uncertain and unstructured driving conditions. Robots in manufacturing, mining, agriculture, space and ocean exploration, and health sciences are just a few examples of challenging applications where human attributes such as cognition and intelligence can play an important role. The proposal for this second special issue on cognitive and neural aspects of robotics with applications was conceived in late 2010, and we are now pleased to present 8 research papers that cover a wide aspect of cognition and intelligence. Initially, we received 15 research papers, but after going through a thorough review process, we have accepted only 8 research papers. These 8 accepted research papers cover some wider aspects of cognition and intelligence in the field of robotics, and for this special issue, we have divided these 8 research papers into three groups. Four research papers cover the fields of cognition, perception, and neural learning in robotics. In the research paper entitled "Control loop sensor calibration using neural networks for robotic control," Kathleen A. Kramer and Stephen C. Stubberud present a technique referred to as a neural extended Kalman filter (NEKF) to provide both state estimation in a control loop and learn the difference between true sensor dynamics and the sensor model. The resulting sensor model provides better estimation capability and redundancy. In the research paper entitled "3D Assembly