ACAT - Learning and Execution of Action Categories

ACAT focuses on the problem how artificial systems (robots) can understand the meaning of information made for humans. For this ACAT generates a dynamic process memory by extraction and storage of action categories from human compatible sources. Action categories include the actual action-encoding but also its context (“background”) and allow for generalization (for example replacement of objects in a given action). Thus, the ACAT system uses action categories to create action sequences (plans). These plans are grounded by perception and execution, which takes place by a robot making use of the generalization properties of the stored action categories. The ultimate purpose is to equip the robot – on an ongoing basis – with abstract, functional knowledge, normally made for humans, about relations between actions and objects leading to a system which can act meaningfully. As industrially very relevant scenario, ACAT uses “instruction sheets” (manuals) made for humans and translates these into a robot-executable format. Similar to computer science, where the development of the first compilers had led to a major step forward, the main impact of ACAT is that the project develops a robot-compiler, which translates human understandable information into a robot-executable program.