Self-supervised learning for soccer ball detection and beyond: interview with winners of the RoboCup 2025 best paper award - Robohub

The article highlights the award-winning research on autonomous soccer ball detection by the SPQR team, who received the best paper award at RoboCup 2025 held in Salvador, Brazil. The team addressed a key challenge in robotic soccer: accurate ball detection under varying conditions. Traditional deep learning approaches require large labeled datasets, which are difficult and labor-intensive to produce for highly specific tasks like RoboCup. To overcome this, the researchers developed a self-supervised learning framework that reduces the need for manual labeling by leveraging pretext tasks that exploit the structure of unlabeled image data. Their method also incorporates external guidance from a pretrained object detection model (YOLO) to refine predictions from a general bounding box to a more precise circular detection around the ball. Deployed at RoboCup 2025, the new model demonstrated significant improvements over their 2024 benchmark, notably requiring less training data and exhibiting greater robustness to different lighting and environmental conditions. This adaptability is crucial given the variability of competition venues. The SPQR team