テーブルを読む：シェフズハットカードゲームのためのSim-to-Realゲーム状態検出
Reading the Table: Sim-to-Real Game State Detection for the Chef's Hat Card Game
3D座標の再投影とドメインランダム化で自動生成した合成データを用い、カード検出・セグメンテーションを組み合わせて実機ロボット上でゲーム状態を推定する手法を開発した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Connor Gäde, Matthias Kerzel, Hauke Bünning, Jan-Gerrit Habekost, Mariana Barros, Stefan Wermter
分類: cs.RO, cs.CV
原文アブストラクト
Tabletop games offer a compelling scenario to study the interaction between artificial and human agents, combining social interaction with game state tracking and physical manipulation. To assess the game state, a robot needs to precisely detect closely related game objects, such as different card designs, requiring dedicated training data that is costly to annotate in the real world. Instead, we develop a fully automated synthetic data collection pipeline, using re-projection of known 3D coordinates to generate labels automatically and domain randomization to bridge the reality gap. This approach is used to collect a dataset of 25413 samples to finetune an object detection and a segmentation model, which are combined to assign detected cards to game-relevant zones and infer the game state. When evaluated on a real NICO humanoid robot, our pipeline achieves an F1-score of 0.906 for card-to-zone assignment, thus providing the perceptual foundation for a robot that can autonomously play Chef's Hat against human players.