DexHoldem Policy Bench - Scene-Preserving Success: leaderboard

Real-robot manipulation for Texas Hold'em with a ShadowHand on a UR10e arm (DexHoldem, Chen et al., 2026): each visuomotor policy is trained on the 1,470 teleoperated demonstrations and run for 80 trials over 14 card and chip primitives. Scene-preserving success rate in percent: only completions that leave the other cards and chips usable count; higher is better.

Metric: Scene-preserving success rate (%; the primitive completes without disturbing other cards or chips, over 80 real-robot primitive trials (10 per pickup primitive, 5 per other primitive, 14 primitives) per policy, each policy trained as one multi-task policy on the 1,470 DexHoldem teleoperated demonstrations). Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 9 models tracked.

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Interactive version: theaggregate.ai/benchmark?slug=dexholdem-policy-bench-scene-preserving-success · How It Works · Data refreshed daily, snapshot 2026-11-12.