GeoMatch Trained Model
This repository contains the final trained GeoMatch model checkpoint developed during my internship at the Human Interactive Robotics (HIRo) Laboratory, IISc Bangalore.
Overview
GeoMatch is a Graph Neural Network (GNN)-based framework for robotic grasp generation. Given an object and a robotic hand, the model predicts feasible grasp poses that can subsequently be evaluated using a physics simulator.
This repository contains the final trained model checkpoint used for inference.
Model File
grasp_gnn.pth
Usage
- Clone the GeoMatch repository:
https://github.com/google-deepmind/geomatch
-
Download
grasp_gnn.pthfrom this repository. -
Replace the default checkpoint (or place it in the appropriate checkpoint directory).
-
Run the GeoMatch inference pipeline to generate grasp predictions.
Related Repository
The code, documentation, bridge implementation, and project handover are available in my GitHub fork:
https://github.com/Saanvi-Mehra/Geomatch
Internship Contributions
The work completed during this internship includes:
- Training the GeoMatch model
- Generating grasp predictions for unseen objects
- Developing a bridge between GeoMatch and GenDexGrasp
- Integrating GeoMatch with the GenDexGrasp evaluation framework in Isaac Gym
- Developing utilities for grasp visualization
- Documenting the complete workflow for future contributors
Note: GenDexGrasp was used only as a physics-based evaluation framework. No modifications were made to its core learning pipeline.
Acknowledgements
This work is based on the original GeoMatch framework developed by Google DeepMind.
Original repository:
https://github.com/google-deepmind/geomatch
Please cite the original GeoMatch work if you use this model.
License
This model is released under the Apache-2.0 License, consistent with the original GeoMatch repository.