CorridorVLA

README.md

@@ -1,3 +1,140 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- Sylvest/LIBERO-plus
+---
+
+## 🌌 CorridorVLA
+
+This repository provides the official implementation of **CorridorVLA**.
+
+> **Direct spatial constraints for Vision-Language-Action models via sparse physical anchors**
+
+[![arXiv](https://img.shields.io/badge/arXiv-2604.21241-b31b1b.svg)](https://arxiv.org/abs/2604.21241)  
+[![Code](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/lidc54/corridorVLA)  
+[![License](https://img.shields.io/badge/License-MIT-green.svg)](#)
+
+---
+
+## 🔍 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**
+
+---
+
+## 🧠 Motivation
+
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/lidc54/corridorVLA/main/assets/motive.png" width="40%">
+</p>
+
+### 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  
+
+---
+
+## 🏗️ Method Overview
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/lidc54/corridorVLA/main/assets/framework.png" width="50%">
+</p>
+
+### 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**
+
+---
+
+## 📊 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**
+
+---
+
+## ⚙️ Implementation
+
+* Built on **[StarVLA](https://github.com/starVLA/starVLA/commit/e1e6457c6cd124248f5ce7b2d3d40fb74f48c6fc)**  
+* Minimal changes:
+
+  * few prediction slots  
+  * loss terms  
+* No heavy architecture redesign  
+
+
+---
+
+## 📌 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**
+
+
+---
+
+
+
+## 📖 Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@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}
+}