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README.md

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-license: mit
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+---
+license: mit
+---
+# Geometric Reasoning Network (GRN)
+
+![GRN model architecture](https://github.com/Smail8/geometric_reasoning_networks/blob/main/assets/model.png?raw=true)
+
+## Model Details
+
+- **Model name:** Geometric Reasoning Network (GRN)
+- **Model type:** Graph Neural Network for robot manipulation feasibility prediction
+- **Framework:** PyTorch, PyTorch Geometric
+- **Associated paper:**  
+  *Learning Geometric Reasoning Networks for Robot Task and Motion Planning*, ICLR 2025
+- **Authors:** Smail Ait Bouhsain, Rachid Alami, Thierry Siméon
+- **License:** See repository LICENSE
+- **Paper:** https://openreview.net/pdf?id=ajxAJ8GUX4
+- **Repository:** https://github.com/smail8/geometric_reasoning_networks
+
+GRN is a learned geometric reasoning model designed to augment **robot Task and Motion Planning (TAMP)** by predicting the feasibility of manipulation actions in cluttered 3D environments.
+
+---
+
+## Model Description
+
+The Geometric Reasoning Network (GRN) operates on **graph-structured representations of manipulation scenes**, encoding objects, candidate grasps, actions, and their geometric relations. It predicts action and grasp feasibility by reasoning jointly over learned geometric constraints.
+
+GRN integrates predictions from auxiliary learned modules:
+- **Inverse Kinematics (IK) feasibility**
+- **Grasp Obstruction (GO)**
+- **Action and Grasp Feasibility (AGF)**
+
+---
+
+## Intended Use
+
+### Primary Use Cases
+- Augmenting robot Task and Motion Planning (TAMP)
+- Learned feasibility prediction for manipulation actions
+- Research in robotic manipulation and geometric reasoning
+- Benchmarking learned planning heuristics
+
+### Out-of-Scope Use
+- Low-level control or trajectory optimization
+- Safety-critical deployment without validation
+- Non-robotic domains
+
+---
+
+## Model Architecture
+
+- Graph Attention Network with message passing
+- Nodes represent objects
+- Edges encode geometric and relational constraints
+- Supervised training on large-scale simulated datasets
+
+---
+
+## Training Details
+
+- **Training data:** GRN simulated manipulation datasets
+- **Loss:** Binary cross-entropy + Mean-squared error
+- **Optimizer:** Adam
+- **Hyperparameters:**
+  | Module    | Batch size | Learning Rate | Epochs |
+  | --------- | ---------- | ------------- | ------ |
+  | IK        | 8192       | 1e-3          | 100    |
+  | GO        | 8192       | 1e-3          | 100    |
+  | AGF       | 2048       | 1e-4          | 100    |
+  | GRN       | 2048       | 1e-4          | 100    |
+ 
+- **Hardware:**  NVIDIA RTX A5000 GPU
+- **Training Time:** 15 hours
+
+---
+
+## Evaluation
+
+Evaluation is performed on both in-distribution and out-of-distribution datasets with increasing scene complexity.
+
+Metrics include:
+- Feasibility prediction accuracy
+- Downstream task and motion planning success rate
+- Generalization to unseen clutter levels and object sizes
+
+GRN outperforms MLP, CNN-based, and standard GNN baselines. Please refer to the paper for detailed results.
+
+![GRN results visualization](https://github.com/Smail8/geometric_reasoning_networks/blob/main/assets/viz.png?raw=true)
+
+---
+
+## Limitations
+
+- Trained exclusively in simulation
+- Performance degrades for extreme clutter or unseen geometries
+- Assumes accurate scene geometry and object poses
+
+---
+
+## Ethical Considerations
+
+This model does not involve human data. Users are responsible for ensuring safe deployment when integrated into physical robotic systems.
+
+---
+
+## Citation
+
+```bibtex
+@inproceedings{ait2025learning,
+  title={Learning Geometric Reasoning Networks for Robot Task and Motion Planning},
+  author={Ait Bouhsain, Smail and Alami, Rachid and Simeon, Thierry},
+  booktitle={The Thirteenth International Conference on Learning Representations},
+  year={2025}
+}