Skip to main content

Collection of robot models for the Ignition Gazebo simulator.

Project description

gym-ignition-models

Robot models for gym-ignition

These models have been mainly tuned and tested to work in Ignition Gazebo.

Installation

This repository can be installed with the pip package manager as follows:

# From PyPI (release versions)
pip3 install gym-ignition-models

# From PyPI (pre-release versions)
pip3 install --pre gym-ignition-models

# From the repository
pip3 install git+https://github.com/robotology/gym-ignition-models.git

Only GNU/Linux distributions are currently supported.

Configuration

Standalone usage

If you use Ignition Gazebo, you need to specify where the models and their dependent resources are located in the filesystem. The simulator reads the IGN_GAZEBO_RESOURCE_PATH environment variable.

Execute the following commands from outside the directory where you cloned this repository to temporarily configure your environment:

PKG_DIR=$(python -c "import gym_ignition_models, inspect, os; print(os.path.dirname(inspect.getfile(gym_ignition_models)))")
export IGN_GAZEBO_RESOURCE_PATH=$PKG_DIR:$IGN_GAZEBO_RESOURCE_PATH

If you want to make this change persistent, add the lines above to your ~/.bashrc.

Note: waiting an upstream fix, you also need to add to IGN_GAZEBO_RESOURCE_PATH all the directories containing model's meshes.

Note: Alternatively, instead of using IGN_GAZEBO_RESOURCE_PATH, you can use SDF_PATH for the models and IGN_FILE_PATH for the meshes.

Python usage

The environment variables are automatically exported when the package is imported. If your application imports also the scenario package, make sure to import gym_ignition_models first.

Usage

You can use these models either with the standalone Ignition Gazebo simulator, or find and import them in your Python code. Here below a Python example of the utility functions provided by the package:

import gym_ignition_models as m

print(f"Models have been installed in: {m.get_models_path()}")
print(f"Available robots: {m.get_robot_names()}")
print()
print("Model files:")

for robot_name in m.get_robot_names():
    print(f"{robot_name}: {m.get_model_file(robot_name)}")

The package also includes a get_model_resource function that provides string, URDF, or SDF descriptions of the supported models. It converts the descriptions internally, if needed.

Supported models

Robot Name Screenshot
ground_plane
cartpole
pendulum
iCubGazeboV2_5
iCubGazeboSimpleCollisionsV2_5
panda
character

License

LGPL v2.1 or any later version.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gym_ignition_models-1.1.1.tar.gz (9.5 MB view details)

Uploaded Source

Built Distribution

gym_ignition_models-1.1.1-py3-none-any.whl (15.1 MB view details)

Uploaded Python 3

File details

Details for the file gym_ignition_models-1.1.1.tar.gz.

File metadata

  • Download URL: gym_ignition_models-1.1.1.tar.gz
  • Upload date:
  • Size: 9.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for gym_ignition_models-1.1.1.tar.gz
Algorithm Hash digest
SHA256 e4e30488988f1ca2efa844d676633241b3d93ac194129cf2114ff7e6075b3e8f
MD5 98016ab8c87c20b81a7b814bd548ebf1
BLAKE2b-256 43cc3ef20a8278cbff8714e642848cfade0d3b3bbd980ec81b401ad9278052de

See more details on using hashes here.

File details

Details for the file gym_ignition_models-1.1.1-py3-none-any.whl.

File metadata

  • Download URL: gym_ignition_models-1.1.1-py3-none-any.whl
  • Upload date:
  • Size: 15.1 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for gym_ignition_models-1.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 946d2a26197285a7f0de09edde030ebdbc143ccda04d0b2d598273afa99c54fb
MD5 7158a432252b2ebbe4422eca7592d845
BLAKE2b-256 06a64ad7c6d85a576550e5bd14da80ebbd1baa1e43104ba9a0c4743188b50f95

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page