robot-manipulation-DRL

This is a PyTorch implementation of reinforcement learning methods (A2C, PPO) in robot manipulation tasks.

For now, the task is robot holding. The robot used is Baxter and the simulation environment is Gazebo.

1. Run simulation environment:

cd catkin_ws
. ./baxter.sh sim
roslaunch baxter_gazebo baxter_world.launch

2. Load humanoid model:

roslaunch baxter_hug humanoid.launch 
rosrun rqt_gui rqt_gui
publish to topic "/humanoid/left_joint_position_controller/command" 0.125*sin(i/200)+0.125

3. Training

cd robot-manipulation-DRL
python scripts/hug_main.py --algo 'a2c'

3. Test

cd robot-manipulation-DRL
python scripts/hug_test.py

###Requirements:

• baxter simulator:

Install baxter simulator as: http://sdk.rethinkrobotics.com/wiki/Simulator_Installation

• gazebo-plugins: (seems useless)

git clone https://github.com/roboticsgroup/roboticsgroup_gazebo_plugins.git
catkin_make --pkg roboticsgroup_gazebo_plugins

• For indigo:

git clone https://github.com/ros-controls/ros_controllers.git
git checkout indigo-devel

modify joint_position_controller.cpp:
    if (!urdf.initParam("humanoid/robot_description"))
    {
        if (!urdf.initParam("robot_description"))
        {
        ROS_ERROR("Failed to parse urdf file");
        return false;
        }
    }
catkin_make --pkg velocity_controllers

Reference:

https://github.com/openai/baselines
https://github.com/ikostrikov/pytorch-a2c-ppo-acktr