🌌 CorridorVLA

Direct spatial constraints for Vision-Language-Action models via sparse physical anchors

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🔍 TL;DR

• Explore an alternative to common visual-style spatial guidance (e.g., predicting future images/videos) using text-style physical anchors
• Predict sparse end-effector Δ-positions
• Use them to impose an explicit corridor constraint on action generation
• Achieves 83.21% success rate on LIBERO-Plus

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🧠 Motivation

Existing VLA paradigm

• Spatial guidance is encoded as visual-style tokens or latent features
• Action generation is influenced indirectly through the backbone features

CorridorVLA

• Predict compact physical quantities (spatial anchors)
• Apply them as direct constraints in the loss
• No need for heavy visual intermediate representations

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🏗️ Method Overview

Key components

(1) Sparse Anchor Prediction

• Predict $K$ future Δ-position anchors
• Represent trajectory structure in a compact form

(2) Action Augmentation

• Concatenate state-related physical quantities (e.g., Δ-positions) to the action vector
• Enable joint prediction of state and action, providing implicit alignment between state space and action space

(3) Corridor Loss

• Defines a tolerance region over the predicted trajectory
• Penalizes deviations outside the region while allowing smooth convergence within it

👉 Behaves like a structured smooth-L1 in trajectory space

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📊 Results

LIBERO-Plus (GR00T-based)

Variant	Description	AVG
base		75.23
c1	query=3	77.25
c2	+ extra data	77.25
c3	+ Δpos anchors	79.21
c4	+ corridor loss (CorridorVLA)	83.21

📈 Improvement:

• +7.98% over baselines
• Largest gain from explicit spatial constraint

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⚙️ Implementation

• Built on StarVLA@e1e6457c6cd124248f5ce7b2d3d40fb74f48c6fc

• Minimal changes:

  • • few prediction slots
  • • loss terms

• No heavy architecture redesign

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📦 Code

👉 https://github.com/lidc54/corridorVLA

# clone repo
git clone https://github.com/lidc54/corridorVLA
cd corridorVLA

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📌 Key Insights

• Spatial guidance can be:

  • explicit (loss-level) instead of implicit (feature-level)

• Physical quantities are:

  • more action-aligned
  • more interpretable

• Simple constraints can:

  • significantly improve stability
  • reduce unstructured exploration

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🙏 Acknowledgements

• Built upon StarVLA
• Inspired by recent advances in VLA and generative action models
• [2026/04/09] 🌟 Huge thanks to Awesome-Latent-Space for including our work in their survey
  “The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook”

⭐ Star Us!

If you find this project useful, consider giving it a ⭐ on GitHub!

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📖 Citation

If you find this work useful, please cite:

@article{corridorvla2025,
  title={CorridorVLA: Explicit Spatial Constraints for Generative Action Heads via Sparse Anchors},
  author={Dachong Li and ZhuangZhuang Chen and Jin Zhang and Jianqiang Li},
  year={2026},
  eprint={2604.21241},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  url={https://arxiv.org/abs/2604.21241}, 
}