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license: mit
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# Geometric Reasoning Networks (GRN)
## Model Details
- **Model name:** Geometric Reasoning Networks (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.
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## 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)**
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## 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
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## Model Architecture
- Graph Attention Network with message passing
- Nodes represent objects
- Edges encode geometric and relational constraints
- Supervised training on large-scale simulated datasets
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## 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
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## 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.
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## Limitations
- Trained exclusively in simulation
- Performance degrades for extreme clutter or unseen geometries
- Assumes accurate scene geometry and object poses
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## Ethical Considerations
This model does not involve human data. Users are responsible for ensuring safe deployment when integrated into physical robotic systems.
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## 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}
}
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