This is the focus of work by and, which won the best paper award at the recent RoboCup symposium . The symposium takes place alongside the annual RoboCup competition, which this year was held in Salvador, Brazil. We caught up with some of the authors to find out more about the work, how their method can be transferred to applications beyond RoboCup, and their future plans for the competition. Could you start by giving us a brief description of the problem that you were trying to solve in your paper "Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots"? The main challenge we faced was that deep learning generally requires a large amount of labeled data. This is not a major problem for common tasks that have already been studied, because you can usually find labeled datasets online.

Over the last decade, the use of robots in production and daily life has increased. Either for supporting workers in challenging tasks, or assisting elderly people and education, robots are no longer solely seen as tireless machines with outstanding motor skills, we also expect them to be intelligent. In fact, one crucial requirement for modern robotic systems is deliberation. Deliberation is a form of intelligence, consisting of "the ability to make decisions which are motivated by reasoning on the available resources, i.e., the capabilities of the robot, the actual description of the environment and the given mission" [1]. Deliberation is a concept from artificial intelligence (AI) which is related to human rationality.

Daniele: Hi, Deep Learning Model; very lovely to meet you. Deep Learning Model: Hi Daniele, I cannot say it is a pleasure -- not sure what that means -- but this interaction is undoubtedly an outlier for me. But please call me 43420a6962c2. Daniele: Oh, ok, interesting name, I guess. Ok, 43420a6962c2, let's get cracking with this interview.