AI RESEARCH
System Design for Maintaining Internal State Consistency in Long-Horizon Robotic Tabletop Games
arXiv CS.AI
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ArXi:2603.25405v1 Announce Type: cross Long-horizon tabletop games pose a distinct systems challenge for robotics: small perceptual or execution errors can invalidate accumulated task state, propagate across decision-making modules, and ultimately derail interaction. This paper studies how to maintain internal state consistency in turn-based, multi-human robotic tabletop games through deliberate system design rather than isolated component improvement.