DexHoldem benchmark tests embodied AI in real-world Texas Hold'em

Researchers have developed DexHoldem, a new benchmark for evaluating embodied AI systems in real-world dexterous manipulation tasks, specifically playing Texas Hold'em. The system includes a ShadowHand for manipulation, a dataset of 1,470 demonstrations, and benchmarks for both primitive skill execution and agentic perception. Initial tests show varying performance across different models, with Opus 4.7 excelling in strict problem-level accuracy for perception and GPT 5.5 leading in average field-wise accuracy, highlighting challenges in integrating perception with policy for closed-loop deployment. AI

影响 Introduces a new physical benchmark for evaluating embodied AI, pushing the development of integrated perception and manipulation systems.

排序理由 Publication of an academic paper introducing a new benchmark for embodied AI systems. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• DexHoldem
• GPT 5.5
• Opus 4.7
• ShadowHand

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