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🦾 A ROS package to run Reinforcement Learning experiments, particularly pick and place tasks, on the TIAGo robot.

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🦾 tiago_gym

A ROS package to run Reinforcement Learning experiments, particularly pick and place tasks, on the TIAGo robot. Uses Gazebo, Rviz and MoveIt! (for motion planning)

rviz-showcase

Installation

Tested on Ubuntu 18.04 only. Beware! Instructions assume familiarity with the ROS packages system.

  • Install ROS Melodic + TIAGo
  • Install openai_ros package into your TIAGo workspace
     cd /home/user/tiago_public_ws/src
     git clone https://bitbucket.org/theconstructcore/openai_ros.git
     cd openai_ros;git checkout version2
     cd /home/user/tiago_public_ws;catkin_make;source devel/setup.bash
  • Install tiago_gym package into your TIAGo workspace
     cd /home/user/tiago_public_ws/src
     git clone https://github.com/edluffy/tiago_gym
     cd /home/user/tiago_public_ws;catkin_make;source devel/setup.bash
  • Launch an environment!
    • roslaunch tiago_gym start_gym.launch
    • Gazebo and Rviz should launch similarly to gif above.
    • To speed up simulation you can run the command gz physics –u 0 -s 0.0025 in a separate terminal.
  • Running custom training scripts
    • Launch with roslaunch tiago_gym start_gym.launch train:=false
    • Run in a separate terminal python my_training_script.py. Example snippets are shown in the Environments section.

Environments

TiagoSimpleEnv-v0 TiagoReachEnv-v0
tiago_gym_simple tiago_gym_reach

TiagoSimpleEnv-v0

This is a simple test environment in which the robot gripper must move to a discrete goal position in 3D space (essentially a 3D gridworld). Example usage:

import tiago_simple_env
from agents import dqn

env = gym.make('TiagoSimpleEnv-v0')
o_dims = len(env.observation_space.sample())
a_dims = env.action_space.n

agent = dqn.DQN(env, input_size=o_dims,
        output_size=o_dims, alpha=0.01, epsilon_decay=0.95)
agent.run(100)
Observations ActionsRewards(Dense!)
0 x-pos of gripper
1 y-pos of gripper
2 z-pos of gripper
0 x-pos of gripper + 0.1
1 x-pos of gripper - 0.1
2 y-pos of gripper + 0.1
3 y-pos of gripper - 0.1
4 z-pos of gripper + 0.1
5 z-pos of gripper - 0.1
Goal within 0.05 10
Else -Distance to goal

TiagoReachEnv-v0

A continuous action environment – robot can move a vector distance in any direction to get to the goal. Example usage:

import tiago_reach_env
from agents import ddpg

env = gym.make('TiagoReachEnv-v0')
o_dims = len(env.observation_space.sample())
a_dims = env.action_space.shape[0]
a_high = env.action_space.high
    
agent = ddpg.DDPG(env, o_dims, a_dims, a_high)
agent.run(100)
Observations ActionsRewards(Dense!)
0 absolute pos of gripper
1 relative pos of gripper
0 x-pos of gripper
1 x-pos of gripper
2 y-pos of gripper
Goal within 0.05 10
Else -Distance to goal

Agents

  • Tensorflow implementations of DQN and DDPG can be found in scripts/agents.

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🦾 A ROS package to run Reinforcement Learning experiments, particularly pick and place tasks, on the TIAGo robot.

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