March 2019

www.smartmachinesandfactories.com March 2019 | 13 | | FEATURES | process over and over again, manufacturing the same component quickly, economically, and at an enormous volume. But this ability to repeat the same function after an initial phase of programming is of no use to smaller scale manufacturing jobs, in which many different components must be produced at a small volume - often referred to in the industry as Batch Size 1. At Batch Size 1, a piece of robotic equipment would need to be programmed ahead of time, enabling it to complete the task in hand. On completion, it would then need to be reprogrammed for the next task, and so on and so forth, ad infinitum. Pre- programming in this manner is a time-consuming task, and repeating the programming phase for each new small job is simply not cost-effective. Even if all of the sequential tasks could be programmed into the robot’s memory ahead of time, the machine would still need to ‘understand’ which part of the job it was currently working on, adjusting its function accordingly, and increasing the potential for error. This would also leave no possibility for learning transferable skills to be used on other jobs in the future. But at Siemens, a team headed by Kai Wurm and Georg von Wichert has come up with a solution to this problem. They have developed a system which will be able to work in the autonomous manner described above, with no need for pre- programming, instead reacting to instructions delivered directly from the operator. This might seem like a regression to Tier I on the AI development spectrum, but consider this for a moment: the tasks that the robot will be completing are incredibly complex, and require a thorough understanding of the aims and objectives of the job at hand. This is not merely reacting to an encountered stimuli; this is interpretation and recognition of instructions delivered, and the translation of these instructions into movements and actions which have been autonomously decided on. Direct evidence for this can be found