Lifelong Object Recognition and Grasp Synthesis using Dual Memory Recurrent Self-Organization Networks
This repository contains the implementation of synthetic sequential point cloud dataset generation and the implementation of hybrid learning architecture, comparises of generative autoencoder and the growing dual memory networks (GDM) for contiual learning of object recognition and grasping synthesis towards lifelong learning fashion.
dataset_generationfolder contains the implementation of sequential point cloud dataset generation in ROS melodic.CNNfolder contains the implementation and evaluation of the proposed autoencoder model.GDM_learningas the name implies, conatins the implementation and evaluation of the updated and modified version of growing dual-memory networks in bacth and incremental learning scenarios.Grasping and dual_pipeline.pycontains the end-end implementation of the proposed system architecture in pybullet simulation environment for pick and place, and pack manipulation tasks.
- python 3.8
- Install the required libraries by running,
pip install -r requirements.txt
The requirements.txt file contains the esssential libraries for both dataset generation and continual learning.
Please follow the instruction given in the Readme.md in the dataset_generation folder to generate the dataset.
A model can be trained using model_training.py script in CNN folder. Based on user input ('train' or 'val').
The instructions to run the GDM model training and testing on the batch and incremental learning scenrios are given in experiments.txt file in GDM_learning folder.
Example for batch learning:
python gdm_learning.py --mode both --test_mode both --learning_type batch --wandb_mode offline --features_path <path to extracted feature vectors> --num_trials 5 --epochs 35 --dist_type manhattan --preprocess 0 --mini_batch 1 --bs_size 50 --context 1 --num_context 2 --output_folder <output folder path/name>
Example for incremental learning:
python gdm_learning.py --mode both --test_mode both --learning_type incremental --wandb_mode offline --features_path <path to extracted feature vectors> --num_trials 5 --epochs 3 --dist_type manhattan --preprocess 0 --mini_batch 1 --bs_size 50 --context 1 --num_context 2 --memory_replay 1 --output_folder <output folder path/name> --e_threshold 0.5 --s_threshold 0.7 --beta 0.4 --e_b 0.5 --e_n 0.005
Run python gdm_learning.py --help to see full list of options.
Note: The above-mentioned experiments need to run inside their respective folders.
In the robot simulation demo, the real-time continous object recognition and grasp synthesis can be tested using dual-pipeline script. Using the script the model can be trained to learn new objects instance and category in contiual lifelong learning manner. Run dual-pipeline.py --help to see the list of options.
Example (for pack scenario):
python dual_pipeline.py --e_network_path <path to episodic memory network> --s_network_path <path to semantic memory network> --model_path <path to trained autoencoder model> --scenario pack
Our simulation robot experiment results, (left) pick and place (pack) mustard bottle to basekt 2 and (right) pack power drill object to basket 1.
Latest version available on Neural Networks (March 2022)
Please adequately refer to the papers any time this code is being used. If you do publish a paper where this work helped your research, we encourage you to cite the following paper in your publications:
@article{SANTHAKUMAR2022167,
title = {Lifelong 3D object recognition and grasp synthesis using dual memory recurrent self-organization networks},
journal = {Neural Networks},
volume = {150},
pages = {167-180},
year = {2022},
issn = {0893-6080},
doi = {https://doi.org/10.1016/j.neunet.2022.02.027},
url = {https://www.sciencedirect.com/science/article/pii/S0893608022000685},
author = {Krishnakumar Santhakumar and Hamidreza Kasaei},
}
Krishnakumar Santhakumar and Hamidreza Kasaei
Work done while at RUG.