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Use AWS RoboMaker and demonstrate a simulation that can train a reinforcement learning model to make a TurtleBot WafflePi to follow a TurtleBot burger, and then Deploy via RoboMaker to the robot.
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README.md

Object Tracker

This Sample Application can train a reinforcement learning model to make a TurtleBot WafflePi to follow a TurtleBot burger. It can then deploy and run the learned model to a real-life TurtleBot WafflePi via AWS RoboMaker.

RoboMaker sample applications include third-party software licensed under open-source licenses and is provided for demonstration purposes only. Incorporation or use of RoboMaker sample applications in connection with your production workloads or a commercial products or devices may affect your legal rights or obligations under the applicable open-source licenses. Source code information can be found here.

Keywords: Reinforcement learning, AWS, RoboMaker

object-tracker-world.jpg

Requirements

  • ROS Kinetic (optional) - To run the simulation locally. Other distributions of ROS may work, however they have not been tested
  • Gazebo (optional) - To run the simulation locally
  • TurtleBot WafflePi (optional) - To run the trained reinforcement learning model in the real world
  • An AWS S3 bucket - To store the trained reinforcement learning model
  • AWS RoboMaker - To run the simulation and to deploy the trained model to the robot

AWS Account Setup

AWS Credentials

You will need to create an AWS Account and configure the credentials to be able to communicate with AWS services. You may find AWS Configuration and Credential Files helpful.

AWS Permissions

To train the reinforcement learning model in simulation, you need an IAM role with the following policy. You can find instructions for creating a new IAM Policy here. In the JSON tab paste the following policy document:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Action": [
                "cloudwatch:PutMetricData",
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents",
                "logs:DescribeLogStreams",
                "s3:Get*",
                "s3:List*",
                "s3:Put*",
                "s3:DeleteObject"
            ],
            "Effect": "Allow",
            "Resource": "*"
        }
    ]
}

Usage (without RoboMaker)

If you plan on using this application with AWS RoboMaker, you can find more detailed instructions in the "Usage with RoboMaker" section.

Training the model

Building the simulation bundle

cd simulation_ws
rosws update
rosdep install --from-paths src --ignore-src -r -y
colcon build
colcon bundle

Running the simulation

The following environment variables must be set when you run your simulation:

  • MARKOV_PRESET_FILE - Defines the hyperparameters of the reinforcement learning algorithm. This should be set to object_tracker.py.
  • MODEL_S3_BUCKET - The name of the S3 bucket in which you want to store the trained model.
  • MODEL_S3_PREFIX - The path where you want to store the model.
  • ROS_AWS_REGION - The region of the S3 bucket in which you want to store the model.
  • AWS_ACCESS_KEY_ID - The access key for the role you created in the "AWS Permissions" section
  • AWS_SECRET_ACCESS_KEY - The secret access key for the role you created in the "AWS Permissions" section
  • AWS_SESSION_TOKEN - The session token for the role you created in the "AWS Permissions" section

Once the environment variables are set, you can run local training using the roslaunch command

source simulation_ws/install/setup.sh
roslaunch object_tracker_simulation local_training.launch

Evaluating the model

Building the simulation bundle

You can reuse the bundle from the training phase again in the simulation phase.

Running the simulation

The evaluation phase requires that the same environment variables be set as in the training phase. Once the environment variables are set, you can run evaluation using the roslaunch command

source simulation_ws/install/setup.sh
roslaunch object_tracker_simulation evaluation.launch

Deploying the model

Building the robot bundle

IMPORTANT You must build the bundle for the same architecture that you plan on running the application on. The easiest way to do this is either by building on the TurtleBot itself or using a cross-build solution, like the one provided with AWS RoboMaker.

Before you build the robot workspace, you must edit the file robot_ws/src/object_tracker_robot/config/model_config.yaml to include the location of your trained model.

Next, you need to add custom rosdep rules required by the application. The first rule is hosted by RoboMaker, and can be added by running echo "yaml https://s3-us-west-2.amazonaws.com/rosdep/python.yaml" > /etc/ros/rosdep/sources.list.d/18-python-boto3-pip.list

The next rule you can create yourself. Create a file /etc/ros/rosdep/custom-rules/object-tracker-rules.yaml and add the following configuration to it.

libjpeg62:
  ubuntu:
    xenial: [libjpeg62]

Then add the rule to rosdep by running echo "yaml file:/etc/ros/rosdep/custom-rules/object-tracker-rules.yaml" > /etc/ros/rosdep/sources.list.d/23-object-tracker-rules.list

Update rosdep once your new rules are added:

sudo apt-get update
rosdep update

Finally, build your application. If your model is not in a private bucket, the build step requires AWS credentials for the bucket you wish to use. One way to do this is by setting the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables. For more information on how to get your AWS credentials, see this page.

cd robot_ws
rosdep install --from-paths src --ignore-src -r -y
colcon build

If you are not running on the same computer you are using to build, you must also bundle your application using colcon bundle --bundle-version 1.

Running the model on the TurtleBot

If you did not build on the robot, you must now copy the bundle to the robot you wish to run on, either by using RoboMaker's deployment features or by copying using scp or rsync.

You must also complete the Raspberry Pi camera setup for the TurtleBot WafflePi, outlined here.

Once the bundle has been uploaded to the target TurtleBot WafflePi, ssh into the TurtleBot and run

export BUNDLE_CURRENT_PREFIX=<bundle location>
source $BUNDLE_CURRENT_PREFIX/setup.sh
roslaunch object_tracker_robot/main.launch

Your TurtleBot WafflePi should now be track and move towards any other TurtleBot you put in front of it! For the best results, your real-life environment should match your simulated environment as closely as possible. You can try tweaking your simulated environment by adding randomization, lighting, textures, or even to have the TurtleBot try to track a different object.

Usage with RoboMaker

Adding a trust policy to your role

If you are using RoboMaker to train the model, you need to add the following trust policy to the role you created in the "AWS Permissions" section. Instructions on how to modify a role can be found here.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Service": "robomaker.amazonaws.com"
            },
            "Action": "sts:AssumeRole"
        }
    ]
}

Building and running the simulation application

You can build and bundle the simulation application the same way you would locally. This produces the artifact simulation_ws/build/output.tar. You'll need to upload these to an s3 bucket, then you can use these files to create a robot application, create a simulation application, and create a simulation job in RoboMaker.

When you create the simulation job in RoboMaker, you must assign a public IP to the simulation, give the simulation subnets and a security group, and set the following environment variables in your simulation application:

  • MARKOV_PRESET_FILE - Defines the hyperparameters of the reinforcement learning algorithm. This should be set to object_tracker.py.
  • MODEL_S3_BUCKET - The name of the S3 bucket in which you want to store the trained model.
  • MODEL_S3_PREFIX - The path where you want to store the model.
  • ROS_AWS_REGION - The region of the S3 bucket in which you want to store the model.

Finally, the launch command for the simulation application is as follows:

source simulation_ws/install/setup.sh
roslaunch object_tracker_simulation local_training.launch

Evaluating the model

To evaluate your model in RoboMaker, clone the job you used to train and change the launch command of the simulation application to the following:

roslaunch object_tracker_robot evaluation.launch

Building the robot bundle

Once the model is trained, you can build a bundle to deploy to the robot. If you are using the RoboMaker Development Environment, you can use the following commands to create a Docker container that performs cross-builds:

cd /opt/robomaker/cross-compilation-dockerfile
sudo bin/build_image.bash
cd ~/environment/ObjectTracker/robot_ws
docker run -v $(pwd):/robot_ws -v ~/.aws:/root/.aws -it ros-cross-compile:armhf
cd robot_ws

Before you build the robot workspace, you must edit the file robot_ws/src/object_tracker_robot/config/model_config.yaml to include the location of your trained model.

Create a file /etc/ros/rosdep/custom-rules/object-tracker-rules.yaml and add the following configuration to it.

libjpeg62:
  ubuntu:
    xenial: [libjpeg62]

Then add the rule to rosdep using the following commands:

echo "yaml file:/etc/ros/rosdep/custom-rules/object-tracker-rules.yaml" > /etc/ros/rosdep/sources.list.d/23-object-tracker-rules.list
apt-get update
rosdep update

Finally, build and bundle your application.

cd /robot_ws
rosdep install --from-paths src --ignore-src -r -y
colcon build
colcon bundle

Deploying the bundle to a robot

Once the application is bundled, you can create a robot application and deploy the bundle to your robot. For more information, see this page.

You must run the following command before deploying the Robot Application to the robot.

sudo chmod 777 /dev/video0

Seeing your robot learn

As the reinforcement learning model improves, the reward function will increase. You can see the graph of this reward function at

All -> AWSRoboMakerSimulation -> Metrics with no dimensions -> Metric Name -> ObjectTrackerRewardPerEpisode

You can think of this metric as an indicator into how well your model has been trained. If the graph has plateaus, then your robot has finished learning.

object-tracker-metrics.png

Troubleshooting

The robot does not look like it is training

The training algorithm has two phases. The first is when the reinforcement learning model attempts to navigate the robot towards its target, while the second is when the algorithm uses the information gained in the first phase to update the model. In the second phase, no new commands are sent to the TurtleBot, meaning it will appear as if it is stopped, spinning in circles, or drifting off aimlessly.

License

Most of this code is licensed under the MIT-0 no-attribution license. However, the sagemaker_rl_agent package is licensed under Apache 2. See LICENSE.txt for further information.

How to Contribute

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