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
This research provides a concrete methodological advancement in VLA model control, addressing a known limitation in current generative action heads and indicating progress towards more precise robotic manipulation.
Improved generative action heads with explicit spatial constraints are critical for realizing robust and reliable real-world applications of AI in robotics and autonomous systems.
The explicit spatial anchoring mechanism introduces a novel and potentially more effective way to guide robotic actions, leading to more predictable and capable systems.
- · Robotics companies
- · AI hardware manufacturers
- · Logistics and manufacturing sectors
- · Companies reliant on primitive automation
- · Inefficient manual labor processes
Increased precision and reliability of robot arms and autonomous agents in unstructured environments.
Accelerated deployment of advanced robotics in critical applications requiring delicate manipulation, like surgery or fine assembly.
Reduced entry barriers for robotic automation across diverse industries due to more intuitive and robust programming and control.
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Read at arXiv cs.AI