An LLM-Based Automatic Sportscast Solution for Robot Soccer Matches

📅 2026-07-16
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🤖 AI Summary
This work addresses the longstanding absence of automated, real-time commentary and statistical analysis in RoboCup robot soccer competitions, which has hindered objective evaluation of technical progress. To overcome this limitation, the authors propose a neuro-symbolic hybrid architecture grounded in large language models that integrates visual kinematic tracking, precise statistical extraction, and controllable natural language generation. The system enables real-time match commentary and post-game analysis for multi-scale humanoid robots competing in shared arenas. Evaluated in both live broadcast and replay scenarios, it consistently produces fluent, hallucination-free, high-quality narratives, substantially enhancing the赛事's data-driven insights and interpretability.
📝 Abstract
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/
Problem

Research questions and friction points this paper is trying to address.

robot soccer
automatic sportscast
RoboCup
real-time commentary
video analysis
Innovation

Methods, ideas, or system contributions that make the work stand out.

LLM-based sports commentary
neuro-symbolic architecture
real-time robot soccer analysis
hallucination-free narration
kinematic-to-language generation
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