CorridorVLA: Explicit Spatial Constraints for Generative Action Heads via Sparse Anchors

arXiv:2604.21241v2 Announce Type: replace-cross Abstract: Vision--Language--Action (VLA) models often use intermediate representations to connect multimodal inputs with continuous control, yet spatial guidance is often injected implicitly through latent features. We propose CorridorVLA, which predicts sparse spatial anchors as incremental physical changes (e.g., end-effector $\Delta$-positions) and uses them to impose an explicit tolerance region in the training objective for action generation. The anchors define a tolerance corridor that guides a flow-matching action head: trajectories whose

Source: arXiv cs.AI — read the full report at the original publisher.

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