Knowledge Representation for Robots through Human-Robot Interaction
This addresses the challenge of enabling robots to perform complex tasks in real-world environments through user interaction, but it appears incremental as it builds on existing interaction and representation methods.
The paper tackles the problem of robots lacking knowledge for complex tasks due to perceptual limitations by proposing a human-robot interaction framework to acquire environmental knowledge, resulting in a representation that enables grounding of referential expressions and topological navigation plans.
The representation of the knowledge needed by a robot to perform complex tasks is restricted by the limitations of perception. One possible way of overcoming this situation and designing "knowledgeable" robots is to rely on the interaction with the user. We propose a multi-modal interaction framework that allows to effectively acquire knowledge about the environment where the robot operates. In particular, in this paper we present a rich representation framework that can be automatically built from the metric map annotated with the indications provided by the user. Such a representation, allows then the robot to ground complex referential expressions for motion commands and to devise topological navigation plans to achieve the target locations.