# DexHoldem：基于灵巧操作系统的德州扑克游戏

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-18 08:00
- AIHOT 分数：69
- AIHOT 链接：https://aihot.virxact.com/items/cmpd04j7h01rpsljlwo21lcvg
- 原文链接：https://arxiv.org/abs/2605.18727

## AI 摘要

研究团队推出了DexHoldem，一个基于ShadowHand机械手和德州扑克的现实世界系统级基准测试平台。平台包含1470个操作演示、物理策略基准和智能体感知基准。测试表明，π0.5模型在操作执行上表现最优，而Opus 4.7与GPT 5.5在感知任务上各有所长，揭示了视觉能力与状态恢复能力之间的差距。闭环案例研究证明感知与策略错误会在实际部署中累积。该平台统一评估了灵巧操作、感知与具身决策能力。

## 正文

Evaluating embodied systems on real dexterous hardware requires more than isolated primitive skills: an agent must perceive a changing tabletop scene, choose a context-appropriate action, execute it with a dexterous hand, and leave the scene usable for later decisions. We introduce DexHoldem, a real-world system-level benchmark built around Texas Hold'em dexterous manipulation with a ShadowHand. DexHoldem provides 1,470 teleoperated demonstrations across 14 Texas Hold'em manipulation primitives, a standardized physical policy benchmark, and an agentic perception benchmark that tests whether agents can recover the structured game state needed for embodied decision making. On primitive execution, π_{0.5} obtains the highest task completion rate (61.2%), while π_{0.5} and π_0 tie on scene-preserving success rate (47.5%). On agentic perception, Opus 4.7 obtains the best strict problem-level accuracy (34.3%), while GPT 5.5 obtains the best average field-wise accuracy (66.8%), exposing a gap between isolated visual sub-capabilities and complete routing-relevant state recovery. Finally, we instantiate the full embodied-agent loop in three case studies, where waiting, recovery dispatches, human-help requests, and repeated primitive execution reveal how perception and policy errors accumulate during closed-loop deployment. DexHoldem therefore evaluates dexterous tabletop execution, agentic perception, and embodied decision routing in a shared physical setting. Project page: https://dexholdem.github.io/Dexholdem/.