Prandroide

Learning and generalization on a navigation task of a wheeled robot.

The objective of this project is to develop several simulation environments for wheeled robots based on existing simulation libraries. The aim is to highlight the problems of transferring learned policies between different simulators. The first simulation environment will be based on the fast simulation library libfastsim. The second environment will be more realistic and will be based on the pybullet library.

demonstration

installation

Run install-dependencies.sh to install the necessary libraries:

• gym
• libfastsim
• pybind11
• pyfastsim
• pybullet-gym
• deap

boost and SDL 1.2 may be necessary for libfastsim.

Scenes

Image	3D Render	Name
		kitchen-v0
		maze_hard-v0
		race_track-v0

blender --background --python pbm_to_obj.py

in the blender folder to convert an pbm image to a 3D object using Blender and Potrace.

Robot

We have modified a model of the iRobot create.

libfastsim

Documentation

documentation can be found here : https://github.com/jbmouret/libfastsim and here for the python binding : https://github.com/alexendy/pyfastsim

Pybullet

Installation

You can install irobot_gym with the following commands:

cd pybullet
pip install -e .

Configuration

each scene is configured in a yml file with its name in the "scenarios" folder like so:

world:
  name: race_track
  sdf: race_track.urdf
  scale: 20 #size of the map in meter
  physics:
    gravity: -9.81
  simulation:
    time_step: 0.016666 #240Hz
    GUI: True
    following_camera: False
  goal:
    goal_position: [2, 5, 0] #x,y,z
    goal_size: 0.2 #diameter of the cylinder

agents:
  id: A
  vehicle:
    name: iRobot
    sensors: [laser]
  task:
    task_name: maximize_progress #type of reward function
    params: { time_limit: 1000.0, goal_size_detection: 0.2 }
  starting_position: [3, 4, 0] #x,y,z
  starting_orientation: [0, 0, 0] #roll,pitch yaw

And for the configuration of the robot:

urdf_file: iRobot.urdf

actuators:
  - type: motor
    name: motor
    params:
      velocity_multiplier: 16.5 # 0.5m/s
      max_force: 20

sensors:
  - type: lidar
    name: lidar
    frequency: 100
    params:
      inaccuracy: 0.00
      rays: 10 # number of lasers
      range: 1 # range in meter
      min_range: 0 # laser start offset
      angle_start: -90 # degree
      angle: 180
      debug: True # visible laser of not

Run

In the pybullet and fastsim folder we have a main.py file used to run each simulation. Witch take for arguments:

• --env : environnement: kitchen, maze_hard, race_track.
• --ctr : choose controller: forward, wall, rule, braitenberg, novelty.
• --sleep_time : sleeping time between each step.
• --save_res : save the result in a csv file: True or False.
• --verbose : verbose for the controller: True or False.
• --file_name : file name of the invidual to load if --ctr=novelty.

Gym

step : [v1,v2] to move the left and right whell.

observation : a list giving the value of each laser.

info :

info['s']	description	return
pose	position of the robot	[x, y, z, roll, pitch, yaw]
acceleration	acceleration of the robot	[x, y, z, roll, pitch, yaw]
velocity	velocity of the robot	[x, y, z, roll, pitch, yaw]
dist_obj	distance to the objectif	a float
time	simulation time	a float

reward, we have different reward functions that can be changed:

• reward_binary_goal_based: a reward of 1 is given when you get close enough to the goal.
• reward_displacement : the reward corresponds to the distance from the previous position.
• reward_rapprochement_goal : the reward corresponds to the distance to the goal.
• no_reward: no reward.