Machine Learning for RealisticBall Detection in RoboCup SPL

In this technical report, we describe the use of a machine learning approach for detecting the realistic black and white ball currently in use in the RoboCup Standard Platform League. Our aim is to provide a ready-to-use software module that can be useful for the RoboCup SPL community. To this end, the approach is integrated within the official B-Human code release 2016. The complete code for the approach presented in this work can be downloaded from the SPQR Team homepage at http://spqr.diag.uniroma1.it and from the SPQR Team GitHub repository at https://github.com/SPQRTeam/SPQRBallPerceptor. The approach has been tested in multiple environments, both indoor and outdoor. Furthermore, the ball detector described in this technical report has been used by the SPQR Robot Soccer Team during the competitions of the Robocup German Open 2017. To facilitate the use of our code by other teams, we have prepared a step-by-step installation guide.

Knowledge Representation for Robots through Human-Robot Interaction

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.