GamerAstra: Supporting 2D Non-Twitch Video Games for Blind and Low-Vision Players through a Multi-Agent Framework
On the map: LLM agents and tool use → Reinforcement learning for LLM agents
Abstract (arXiv)
Blind and low-vision (BLV) players face critical challenges in engaging with video games due to the inaccessibility of visual elements, difficulties navigating interfaces, and limitations in performing interaction. Meanwhile, the development of specialized accessibility features typically requires substantial programming effort and is often implemented on a game-by-game basis. To address these challenges, we introduce GamerAstra, a multi-agent human-AI collaboration framework that leverages a multi-agent design to facilitate access to 2D non-twitch video games for BLV players. It integrates vision-language models and computer vision techniques, enabling interaction with games lacking native accessibility support. The framework also incorporates custom assistance granularities to support varying degrees of visual impairment and enhances interface navigation through multiple input modalities. Technical evaluations and user studies indicate that GamerAstra effectively enhances playability and provides a more immersive gaming experience for BLV players. These findings also underscore potential avenues for advancing intelligent accessibility frameworks in the gaming domain.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 22 of its 65 references that are in the map. First posted on arXiv 28 Jun 2025.
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Other references · 21
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9 more
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- 2021NavStick: Making Video Games Blind-Accessible via the Ability to Look Aroundcited by 3
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- 2025How Users Who are Blind or Low Vision Play Mobile Games: Perceptions, Challenges, and Strategiescited by 3
- 2024DesignChecker: Visual Design Support for Blind and Low Vision Web Developerscited by 2
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