Imitation Learning for the Transfer of Human Skills to Industrial Trucks

Industrial trucks can automatically transport load carriers, such as pallets, in logistics environments. However, automated load handling requires sufficiently precise positioning and alignment of the load carriers in line with the process. Complex situations during storage and retrieval, where automated systems have so far reached their limits, are usually easily solved by human drivers – thanks to their skills such as flexibility, speed of action, comprehension and experience.

The LernFFZ research project aims to transfer these skills, which were previously reserved for humans, to automated AGVs for the first time thanks to artificial intelligence (AI). To this end, the implicit knowledge of experienced drivers is recorded and formalized by means of information fusion of driving data recording and environment detection. Based on this, the AI of a semi-automated AGV will be trained to prescribe and independently execute driving movements based on the implicit knowledge of experienced drivers (imitation learning).

As part of the LernFFZ research project, IPH is developing synthetic training data sets based on an intralogistics simulation environment, which record and reflect expert knowledge with the help of a simulator. Furthermore, IPH supports the development of imitation learning algorithms and the simulation-based evaluation of these algorithms.

Publications about the project