A novel technique to improve the learning ability of robots that perform repetitive tasks

Learning from one's past mistakes is not limited to humans. Computers do it, too. In industries, this is done via computer-based control systems that help operate production systems. For industrial robots that perform specific tasks in batches, say producing clothing, computer chips, or baked goods, the most commonly used control technique is iterative learning control (ILC). Most industries still rely on ILC systems that use a learning strategy called the proportional-type update rule (PTUR). This technique improves the performance of ILC systems by repeating the same task over and over and updating its control input based on errors encountered in previous iterations.