Adaptive Machines for Industrial Automation

machines that can continually learn from experiences and apply previously learned knowledge to novel situations. Artificial Intelligence (AI) methods are becoming part of our daily and can now outperform humans in many domains. However, these systems still pale in comparison to even simple biological intelligence, which can learn and adapt to unforeseen experiences. Current machine learning systems can only deal with situations they have been trained for in advance; they are unable to adapt during execution to unexpected events. Organisms in nature can react quickly to changes because they have evolved innate properties that reflect properties of the physical world. Here we will develop a new class of algorithms based on a combination of evolutionary computation and state-of-the-art reinforcement learning, which will significantly extend the usefulness of robots in manufacturing and warehouse automation.