Temporal emphasis for goal extraction in task demonstration to a humanoid robot by naive users
Goal extraction in learning by demonstration is a complex problem. A novel approach, inspired by developmental psychology and focused on use in experiments with naive users, is presented in this paper. Participants were presenting a simple task, how to stack three boxes, to the humanoid robot iCub. The stationary states of the task - 1 box, 2 boxes stacked, 3 boxes stacked - were defined and the time span of each state was measured. Analysis of the results showed that there is a significant result that users tend to keep the boxes stationary longer upon completion of the end goal than upon completion of the sub-goals. A simple and straightforward learning algorithm was then used on the demonstration data, using only the time spans of the stationary states. The learning algorithm successfully detected the end goal. These temporal differences, functioning as emphasis, could be used as a complementary mechanism for goal extraction in imitation learning. Furthermore, it is suggested that since a simple, straightforward learning algorithm can use these pauses to recognise the goal state, humans may also be able to use this pause as a complementary mechanism for recognising the goal state of a task.