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license: mit |
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# Geometric Reasoning Networks (GRN) |
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## Model Details |
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- **Model name:** Geometric Reasoning Networks (GRN) |
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- **Model type:** Graph Neural Network for robot manipulation feasibility prediction |
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- **Framework:** PyTorch, PyTorch Geometric |
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- **Associated paper:** |
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*Learning Geometric Reasoning Networks for Robot Task and Motion Planning*, ICLR 2025 |
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- **Authors:** Smail Ait Bouhsain, Rachid Alami, Thierry Siméon |
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- **License:** See repository LICENSE |
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- **Paper:** https://openreview.net/pdf?id=ajxAJ8GUX4 |
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- **Repository:** https://github.com/smail8/geometric_reasoning_networks |
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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 |
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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. |
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GRN integrates predictions from auxiliary learned modules: |
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- **Inverse Kinematics (IK) feasibility** |
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- **Grasp Obstruction (GO)** |
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- **Action and Grasp Feasibility (AGF)** |
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## Intended Use |
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### Primary Use Cases |
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- Augmenting robot Task and Motion Planning (TAMP) |
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- Learned feasibility prediction for manipulation actions |
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- Research in robotic manipulation and geometric reasoning |
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- Benchmarking learned planning heuristics |
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### Out-of-Scope Use |
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- Low-level control or trajectory optimization |
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- Safety-critical deployment without validation |
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- Non-robotic domains |
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## Model Architecture |
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- Graph Attention Network with message passing |
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- Nodes represent objects |
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- Edges encode geometric and relational constraints |
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- Supervised training on large-scale simulated datasets |
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## Training Details |
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- **Training data:** GRN simulated manipulation datasets |
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- **Loss:** Binary cross-entropy + Mean-squared error |
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- **Optimizer:** Adam |
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- **Hyperparameters:** |
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| Module | Batch size | Learning Rate | Epochs | |
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| --------- | ---------- | ------------- | ------ | |
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| IK | 8192 | 1e-3 | 100 | |
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| GO | 8192 | 1e-3 | 100 | |
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| AGF | 2048 | 1e-4 | 100 | |
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| GRN | 2048 | 1e-4 | 100 | |
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- **Hardware:** NVIDIA RTX A5000 GPU |
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- **Training Time:** 15 hours |
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## Evaluation |
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Evaluation is performed on both in-distribution and out-of-distribution datasets with increasing scene complexity. |
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Metrics include: |
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- Feasibility prediction accuracy |
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- Downstream task and motion planning success rate |
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- Generalization to unseen clutter levels and object sizes |
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GRN outperforms MLP, CNN-based, and standard GNN baselines. Please refer to the paper for detailed results. |
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## Limitations |
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- Trained exclusively in simulation |
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- Performance degrades for extreme clutter or unseen geometries |
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- Assumes accurate scene geometry and object poses |
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## Ethical Considerations |
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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 |
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```bibtex |
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@inproceedings{ait2025learning, |
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title={Learning Geometric Reasoning Networks for Robot Task and Motion Planning}, |
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author={Ait Bouhsain, Smail and Alami, Rachid and Simeon, Thierry}, |
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booktitle={The Thirteenth International Conference on Learning Representations}, |
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year={2025} |
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} |
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