Bottom-up learning of object categories, action effects and logical rules: From continuous manipulative exploration to symbolic planning

摘要

This work aims for bottom-up and autonomous development of symbolic planning operators from continuous interaction experience of a manipulator robot that explores the environment using its action repertoire. Development of the symbolic knowledge is achieved in two stages. In the first stage, the robot explores the environment by executing actions on single objects, forms effect and object categories, and gains the ability to predict the object/effect categories from the visual properties of the objects by learning the nonlinear and complex relations among them. In the next stage, with further interactions that involve stacking actions on pairs of objects, the system learns logical high-level rules that return a stacking-effect category given the categories of the involved objects and the discrete relations between them. Finally, these categories and rules are encoded in Planning Domain Definition Language (PDDL), enabling symbolic planning. We realized our method by learning the categories and rules in a physics-based simulator. The learned symbols and operators are verified by generating and executing non-trivial symbolic plans on the real robot in a tower building task.