This research project focuses on developing adaptable industrial solutions to enable the transition from conventional, rigidly programmed systems to intelligent, AI-driven ones. The focus is on complex industrial activities that still depend heavily on human skills, particularly in the context of manufacturing and warehouse logistics.

In manufacturing, the project targets tasks such as surface finishing and burr removal. Despite widespread automation, these processes still require human expertise to manage variability and achieve the desired quality in many areas. The research seeks to equip robotic manipulators with AI capabilities to perceive, learn, and adapt their actions.

In warehouse logistics and packaging, the project addresses the need to increase productivity while reducing human operators' physical and mental workload. Order picking is still largely manual today, with workers selecting and placing products into boxes. The research focuses on developing autonomous robotic systems that receive order instructions and perform pick-and-place tasks. These robots use advanced vision systems to identify optimal gripping points and reconfigurable grippers to handle diverse-shaped items.