Towards Human-Level Semantics Understanding of Human-Centered Object Manipulation Tasks for HRI: Reasoning About Effect, Ability, Effort and Perspective Taking

In its lifetime, a robot should be able to autonomously understand the semantics of different tasks to effectively perform them in different situations. In this context, it is important to distinguish the meaning (in terms of the desired effect) of a task and the means to achieve that task. Our focus is those tasks in which one agent is required to perform a task for another agent, such as give, show, hide, make-accessible, etc. In this paper, we identify that a high-level human-centered combined reasoning, based on perspective taking, efforts and abilities analyses, is the key to understand semantics of such tasks. By combining these aspects, the robot infers sets of hierarchy of facts, which serve for analyzing the effect of a task. We adapt the explanation based learning approach enabling the task understanding from the very first demonstration and continuous refinement with new demonstrations. We argue that such symbolic level understanding of a task, which is not bound to trajectory, kinematics structure or shape of the robot, facilitates generalization to novel situations as well as ease the transfer of acquired knowledge among heterogeneous robots. Further, the knowledge of tasks at such human understandable level of abstraction will enrich the natural human–robot interaction.