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Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models

The paper introduces ACG-Bench, a benchmark for evaluating arm-wise compositional generalization in dual-arm vision-language-action models. It explores architectural strategies like arm-token grouping, skill-specific LoRA adapters, and arm-wise attention, showing that combining these improves generalization success in both simulation and physical robot settings.

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PublishedOctober 5, 2026Zaibin Zhang, Binghao Ran, Yuhan Wu
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WHY IT MAY MATTER

This research provides insights into improving the generalization of dual-arm robotic systems by analyzing architectural choices and training strategies, which can help in developing more adaptable and efficient multi-arm collaboration systems.

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