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VLA-Fabric: Learning Composable Interaction Mechanisms for Multi-Arm VLA Agents

VLA-Fabric assigns a complete vision-language-action (VLA) model to each robotic arm, preserving each arm's local perception and action ownership, and achieves coordination through explicitly learned interaction across policy boundaries. The paper systematically studies where interaction should occur, how peer information should be aggregated, and how interaction should be learned, finding that shared context, peer-specific latent variables, and action-stage interaction play complementary coordination roles, and reproduces this principle on structurally different VLA architectures.

Part of the event: VLA-Fabric: Multi-arm VLA improves coordination success rates through cross-policy interaction

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