An LLM-Based Automatic Sportscast Solution for Robot Soccer Matches
For the RoboCup community, it provides a tool to automatically track progress and generate commentary, but the approach is incremental as it combines existing techniques.
The paper introduces a fully autonomous, real-time sports commentator for RoboCup soccer matches that extracts statistics from video streams and generates hallucination-free narration, adapting to dynamic multi-robot fields.
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/