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

By PulseAugur Editorial · [1 sources] ·

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

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

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

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

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

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

  1. arXiv cs.AI TIER_1 English(EN) · Yi Ma ·

    DexHoldem: Playing Texas Hold'em with Dexterous Embodied System

    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 introdu…

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