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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- robotics |
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- vision-language-action |
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- vla |
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- flow-matching |
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- clip |
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- manipulation |
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- pick-and-place |
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- educational |
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- lightweight |
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- beginner-friendly |
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library_name: transformers |
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pipeline_tag: robotics |
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datasets: |
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- synthetic-pick-place-1k |
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metrics: |
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- mae |
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- position_error |
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- quaternion_error |
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- accuracy |
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--- |
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# 🎓 Minimal VLA: The Simplest Vision-Language-Action Model |
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> **The lightest VLA implementation for learning and experimentation — only ~20MB!** |
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A beginner-friendly, minimal Vision-Language-Action (VLA) model designed for **educational purposes** and **rapid prototyping**. This project demonstrates the core concepts of VLA systems using CLIP + Flow Matching in the simplest possible setup. |
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## ✨ Why This Project? |
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| Feature | This Model | Typical VLAs | |
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|---------|-----------|--------------| |
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| **Model Size** | **~20MB** | 1-7GB+ | |
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| **Training Time** | **~20 min** | Hours to days | |
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| **Hardware** | Any GPU / CPU | High-end GPUs | |
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| **Simulation** | 2D rendering | Physics engines | |
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| **Complexity** | ~1000 lines | 10,000+ lines | |
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| **Dependencies** | PyTorch + CLIP | Complex stacks | |
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**Perfect for:** |
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- 🎓 **Students** learning VLA fundamentals |
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- 🔬 **Researchers** prototyping new ideas quickly |
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- 👨🏫 **Educators** teaching robot learning concepts |
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- 🚀 **Developers** building their first VLA system |
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## 🏗️ Model Overview |
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This minimal VLA predicts 8-DOF robotic actions from RGB images and natural language: |
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``` |
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Input: Image (224×224) + Text ("pick up the red cube") |
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↓ |
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CLIP ViT-B/32 (frozen, vision + language encoding) |
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↓ |
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Flow Matching Policy (~2MB trainable parameters) |
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↓ |
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Output: [x, y, z, qx, qy, qz, qw, gripper] |
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``` |
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### Key Design Choices for Simplicity |
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1. **Frozen CLIP Backbone** — No need to train vision-language understanding |
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2. **2D Synthetic Environment** — No physics engine required |
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3. **Flow Matching** — Elegant generative approach for continuous actions |
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4. **Separate Gripper Classifier** — Binary decision for open/close |
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## 📊 Performance |
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Evaluated on 10 test samples from 1000 synthetic demonstrations: |
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| Metric | Value | Notes | |
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|--------|-------|-------| |
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| Position Error | **8.60cm** | Suitable for ~5cm cube picking | |
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| Gripper Accuracy | **75%** | Reliable grasp planning | |
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| Overall MAE | **0.1217** | Across all 8 action dimensions | |
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| Quaternion Error | 19.36° | Best for top-down grasps | |
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> ⚠️ **Note**: This is an educational model trained on simplified 2D projections. Real-world deployment requires fine-tuning on actual robot data. |
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## 🚀 Quick Start |
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### Installation |
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```bash |
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pip install torch transformers pillow numpy matplotlib |
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``` |
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### Inference (3 lines!) |
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```python |
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from vla_flow_matching import VLM_Encoder, ImprovedFlowMatchingPolicy |
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import torch |
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# Load model |
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device = 'cuda' if torch.cuda.is_available() else 'cpu' |
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checkpoint = torch.load('vla_checkpoint_best.pt', map_location=device) |
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vlm_encoder = VLM_Encoder().to(device) |
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policy = ImprovedFlowMatchingPolicy(action_dim=8, context_dim=1024, hidden_dim=512).to(device) |
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policy.load_state_dict(checkpoint['policy']) |
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policy.eval() |
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# Predict! |
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from PIL import Image |
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image = Image.open('workspace.jpg').resize((224, 224)) |
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context = vlm_encoder.encode([image], ["pick up the red cube"]) |
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action = policy.sample(context, num_samples=1, device=device) |
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print(f"Position: {action[0, :3].cpu().numpy()}") |
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print(f"Gripper: {'CLOSE' if action[0, 7] > 0 else 'OPEN'}") |
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``` |
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### Train from Scratch (~20 minutes) |
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```bash |
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# Step 1: Generate synthetic data |
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python vla_flow_matching.py --mode generate_data --num_demos 1000 |
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# Step 2: Train (takes ~20 min on consumer GPU) |
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python vla_flow_matching.py --mode train --epochs 200 --batch_size 32 |
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# Step 3: Evaluate |
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python vla_flow_matching.py --mode replay --checkpoint vla_checkpoint_best.pt |
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``` |
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## 📁 Repository Structure |
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``` |
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├── vla_flow_matching.py # Complete implementation (~1000 lines) |
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├── vla_checkpoint_best.pt # Trained weights (~20MB) |
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├── demo_data.pkl # Training data (1000 demos) |
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├── replay_results.png # Evaluation visualization |
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└── README.md # This file |
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``` |
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## 🎯 What You'll Learn |
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This codebase teaches core VLA concepts: |
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1. **Vision-Language Encoding**: Using CLIP for joint image-text understanding |
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2. **Flow Matching**: A modern generative approach for action prediction |
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3. **Action Representation**: 8-DOF with quaternion rotations |
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4. **Synthetic Data Generation**: Creating training environments without physics |
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5. **Model Architecture**: Combining frozen backbones with trainable policies |
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## 🔧 Architecture Details |
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### VLM Encoder (Frozen CLIP) |
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- Vision: ViT-B/32 → 512-dim features |
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- Text: Transformer → 512-dim features |
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- Combined: 1024-dim context vector |
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### Flow Matching Policy (~2MB) |
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``` |
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Context Encoder: 1024 → 512 → 128 (with LayerNorm, GELU, Dropout) |
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Time Embedding: Sinusoidal 128-dim |
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Action Encoder: 7D → 128 |
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Velocity Network: 384 → 512 → 256 → 7 |
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``` |
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### Gripper Classifier |
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``` |
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Context → 512 → 256 → 2 (softmax) |
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``` |
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### Training Configuration |
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```yaml |
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Epochs: 200 |
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Batch Size: 32 |
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Learning Rate: 1e-4 (cosine decay to 1e-5) |
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Optimizer: AdamW (weight_decay=1e-4) |
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Flow Steps: 200 (Euler integration) |
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``` |
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## 🌈 Training Data |
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The synthetic environment generates pick-and-place demonstrations: |
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- **1000 demonstrations** with diverse object positions |
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- **6 cube colors**: red, blue, green, yellow, purple, orange |
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- **24 instruction templates**: "pick up the [color] cube", "grasp the [color] block", etc. |
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- **40cm × 40cm workspace** with position and orientation variations |
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- **2D projection with 3D visual effects** (shadows, shading) |
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## ⚡ Extending This Work |
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### Ideas for Students/Researchers |
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1. **Add more objects**: Extend beyond cubes to spheres, cylinders |
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2. **Multi-step tasks**: Chain pick → place actions |
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3. **Real images**: Fine-tune on real robot data |
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4. **Better orientation**: Improve quaternion prediction accuracy |
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5. **Action chunking**: Predict action sequences instead of single steps |
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6. **Physics simulation**: Replace 2D rendering with PyBullet/MuJoCo |
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### Fine-tuning for Real Robots |
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```bash |
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# Collect 10-50 real demonstrations, then: |
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python vla_flow_matching.py --mode finetune \ |
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--checkpoint vla_checkpoint_best.pt \ |
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--data_path real_robot_demos.pkl \ |
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--epochs 30 --lr 1e-5 |
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``` |
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## ⚠️ Limitations |
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This is an **educational model** with intentional simplifications: |
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- ❌ 2D synthetic environment (no physics) |
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- ❌ Single-object scenes only |
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- ❌ Limited orientation precision |
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- ❌ Not suitable for direct real-world deployment |
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- ❌ No temporal/sequential reasoning |
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**Do NOT use for**: Safety-critical applications, precision assembly, or autonomous operation without extensive testing. |
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## 🙏 Acknowledgments |
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Built with: |
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- 🤗 Transformers (CLIP) |
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- 🔥 PyTorch |
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- 📊 NumPy & Matplotlib |
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Inspired by: |
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- [Flow Matching](https://arxiv.org/abs/2210.02747) (Lipman et al., 2023) |
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- [CLIP](https://arxiv.org/abs/2103.00020) (Radford et al., 2021) |
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- [RT-1](https://arxiv.org/abs/2212.06817) (Brohan et al., 2022) |
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- [OpenVLA](https://openvla.github.io/) (Kim et al., 2024) |
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## 📚 Citation |
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```bibtex |
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@misc{minimal-vla-2025, |
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title={Minimal VLA: A Lightweight Vision-Language-Action Model for Education}, |
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author={LeTau}, |
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year={2025}, |
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publisher={Hugging Face}, |
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url={https://huggingface.co/your-username/minimal-vla} |
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} |
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``` |
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## 📄 License |
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MIT License — Feel free to use, modify, and share! |
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--- |
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**Questions?** Open an issue or reach out. Happy learning! 🤖 |
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