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.