This project explores the implementation of obstacle avoidance in autonomous systems using imitation learning. The aim is to train agents to navigate environments while effectively avoiding obstacles.
GAIL combines generative adversarial networks with imitation learning to train agents to replicate expert behavior without explicit supervision of actions.
Adversarial Framework:
Training Process:
Reward Signal:
GAIL is beneficial in scenarios where defining reward functions is challenging, allowing agents to learn complex behaviors through expert demonstrations.
To build your ROS package, follow these steps:
imitation_learning_ros
folder:
cd imitation_learning_ros
colcon build
source install/setup.bash
Launch the Simulation: Use the following command to launch the Gazebo simulation:
cd imitation_learning_ros
source install/setup.bash
ros2 launch robot_gazebo main.launch.xml
Run the Data Collection Script: Use the following command to execute the collect_data.py script:
ros2 run imitation_learning collect_data.py
To train the imitation learning model, you will need to run the train_imitation_learning.py script. Follow these steps:
Navigate to Your ROS 2 Workspace: Ensure you are in the root directory of your ROS 2 workspace. If you are in the imitation_learning_ros folder, you may need to go up a level:
source install/setup.bash
Run the Training Script: Use the following command to execute the train_imitation_learning.py script:
ros2 run imitation_learning train_imitation_learning.py
The train_imitation_learning.py script is responsible for training the agent using the collected data. It typically loads the dataset of state-action pairs gathered during the data collection phase and utilizes algorithms like GAIL or behavioral cloning to optimize the agent's policy. The training process may involve tuning hyperparameters and evaluating the model's performance based on its ability to mimic expert behaviors.
If you encounter issues while running the training script, check the terminal output for error messages. Ensure that the collected data is available and correctly formatted.
Example Command Sequence Here’s a quick sequence of commands you might use:
cd .. # Navigate to the ROS 2 workspace root
source install/setup.bash
ros2 run imitation_learning train_imitation_learning.py
This project demonstrates how imitation learning can be effectively utilized for obstacle avoidance in robotics. Future work may involve real-world testing and further model refinement.