← 研踪 PaperTrailarXiv:2611.15524 · v1
Reading the Table: Sim-to-Real Game State Detection for the Chef's Hat Card Game
首次提交 2026-11-11 22:14
版本更新 2026-11-11 22:14
雷达观测 2026-11-13 11:11
摘要
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.
作者备注：Presented at the Dungeons, Neurons, and Dialogues workshop (DnD-SIDC 2026) at ICANN 2026, https://dnd-sidc.github.io/
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v1 · 2026-11-11 22:14 · 最新观测版本
Reading the Table: Sim-to-Real Game State Detection for the Chef's Hat Card Game
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.
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