EMBODIED CONCEPT DISCOVERY THROUGH QUALITATIVE ACTION MODELS
Aljaž Košmerlj, Ivan Bratko, Jure Žabkar · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 2011
We present a novel approach to embodied learning of qualitative models. We introduce algorithm STRUDEL that enables an autonomous robot to discover new concepts by performing experiments in its environment. The robot collects data about its actions and its observations of the environment. From the obtained data, the robot learns qualitative descriptive models of the effects that its actions have in the environment. Models are learned using inductive logic programming. We describe two experiments with a humanoid robot Nao in which Nao learns descriptive qualitative models which contain what can be interpreted as simple definitions of the concepts of movability and stability.