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.
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.
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
@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}
}