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AI-based Modeling and Control of Robotic Systems

This repository provides the python codes for the AI based modeling and control of a 2-link robotic manipulator. The user can develop the Deep Neural Network (DNN) based inverse dynamics model and Reinforcement Learning (RL) based target reaching controller of a robotic arm.

Paper

A paper on this work can be found at https://ieeexplore.ieee.org/abstract/document/9546974

Raina, Deepak, and Subir Kumar Saha. "AI-Based Modeling and Control of Robotic Systems: A Brief Tutorial." 2021 3rd International Conference on Robotics and Computer Vision (ICRCV). IEEE, 2021.

@inproceedings{raina2021ai,
  title={AI-Based Modeling and Control of Robotic Systems: A Brief Tutorial},
  author={Raina, Deepak and Saha, Subir Kumar},
  booktitle={2021 3rd International Conference on Robotics and Computer Vision (ICRCV)},
  pages={45--51},
  year={2021},
  organization={IEEE}
}

Video Link: https://youtu.be/yembjBOEUOI

AI based modeling of the 2-link arm

The modeling task is the DNN based modeling of the 2-link robotic arm to estimate the desired torque values for the given input joint trajectory.

Installation

  • Install PyTorch, which is a GPU and CPU optimised tensor library for deep learning. Refer here
pip install torch
  • Install Python 3.6 (although other Python 3.x versions may still work). You can either download Python 3.6 here, or use pyenv to install Python 3.6 in a local directory.
pip install python=3.6

Getting started

1. Training the NN: To train the DNN for model learning, use the train_test.py file. The class has input called Epoch which needs to be mentioned. The training and test loss is observed at the end of training.

2. Testing the NN: Use the same train_test.py file to test the model after the successful completion of training. The output observed is the computation time for analytical model and DNN model.

An example for training and testing the model is given below:

from controller import RobotController
# Robot controller
controller = RobotController(recordData=False)
EPOCHS = 5000
MODEL_FILE_LOC = 'models/trained_nn_model_' + str(EPOCHS)
## Training
controller.train(epochs=EPOCHS)
## Testing
controller.test(model_fileloc = MODEL_FILE_LOC, num_test=1)

AI based control of 2-link arm

The two state-of-the-art RL algorithms i.e DDPG (Deep Deterministic Gradient Policy) and PPO (Proximal Policy Optimization) are used in learning a target reaching controller of robotic arm.

Installation

pip install gym

Getting started

1. Training the Model: Use the file test_train.py for training the model. The controller class takes seed and reward type as input. The controller.train() class function takes inputs like number of episodes and maximum time steps.

2. Testing the model: Use the same file to test the model. The output is the 2-link arm reaching the target ball. The controller.test() takes inputs as the maximum timesteps and number of tests to be run.

An example for training and testing the controller using DDPG algorithm is given below:

from agent import Controller
SEED = 4
REWARD_TYPE = 2
controller = Controller(rand_seed = SEED, rew_type = REWARD_TYPE)
# Training
NUM_EPISODES = 10000
MAX_TIME_STEPS = 150
MODEL_SAVE_NAME = 'reacher'
controller.train(num_episodes = NUM_EPISODES, max_timesteps = MAX_TIME_STEPS, model_name = MODEL_SAVE_NAME)
# Testing
NUM_TESTS = 1
MODEL_LOAD_NAME = 'reacher_' + str(NUM_EPISODES) + '_' + str(REWARD_TYPE)
controller.test(num_test = NUM_TESTS, max_timesteps = MAX_TIME_STEPS, model_name = MODEL_LOAD_NAME)