An LLM-Based Automatic Sportscast Solution for Robot Soccer Matches
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Abstract (arXiv)
RoboCup has always been a scenario to develop systems that solve real-world problems. Driven by the main goal of playing against the 2050 FIFA World Cup champions, the RoboCup Soccer leagues need to constantly measure how the research community is progressing. Computing visual statistics from match videos is a crucial way to track this evolution. To address this challenge, this paper introduces a fully autonomous, real-time sports commentator for RoboCup matches. By bridging the gap between raw kinematic tracking and natural language generation, our neuro-symbolic architecture extracts precise statistics from video streams and turns them into fluent, hallucination-free narration. The proposed system is capable of generating statistics and commentary both during live match streaming and in post-game analysis, easily adapting to the new dynamism of the league where different humanoid robots of different sizes share the field. Supplemental materials are available at https://lab-rococo-sapienza.github.io/MARIO/
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References · 10
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2025SAM 3: Segment Anything with Conceptscited by 835
- 2025YOLOv12: Attention-Centric Real-Time Object Detectorscited by 300
- 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
- 2022SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videoscited by 80
- 2024MatchTime: Towards Automatic Soccer Game Commentary Generationcited by 37
- 2021SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domaincited by 4
- 2024LLCoach: Generating Robot Soccer Plans using Multi-Role Large Language Modelscited by 3
- 2025From Multimodal Perception to Strategic Reasoning: A Survey on AI-Generated Game Commentarycited by 3
- 2022MARIO: Modular and Extensible Architecture for Computing Visual Statistics in RoboCup SPLcited by 1
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