Omniverse Isaac Gym environments

Environments

The repository https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs provides the example reinforcement learning environments for Omniverse Isaac Gym

These environments can be easily loaded and configured by calling a single function provided with this library. This function also makes it possible to configure the environment from the command line arguments (see configuration-and-command-line-arguments) or from its parameters as a python dictionary

Additionally, multi-threaded environments can be loaded. These are designed to isolate the RL policy in a new thread, separate from the main simulation and rendering thread. Read more about it in the NVIDIA Omniverse Isaac Sim documentation: Multi-Threaded Environment Wrapper

Note

The command line arguments has priority over the function parameters

Note

Only the configuration related to the environment will be used. The configuration related to RL algorithms are discarded since they do not belong to this library

Note

Omniverse Isaac Gym environments implement a functionality to get their configuration from the command line. Setting the headless option from the trainer configuration will not work. In this case, it is necessary to invoke the scripts as follows: python script.py headless=True

Basic usage

Common environments

In this approach, the RL algorithm maintains the main execution loop

# import the environment loader
from skrl.envs.torch import load_omniverse_isaacgym_env

# load environment
env = load_omniverse_isaacgym_env(task_name="Cartpole")

# import the environment loader
from skrl.envs.torch import load_omniverse_isaacgym_env

# load environment
env = load_omniverse_isaacgym_env()

Run the main script passing the configuration as command line arguments. For example:

python main.py task=Cartpole

Multi-threaded environments

In this approach, the RL algorithm is executed on a secondary thread while the simulation and rendering is executed on the main thread

import threading

# import the environment loader
from skrl.envs.torch import load_omniverse_isaacgym_env

# load environment
env = load_omniverse_isaacgym_env(task_name="Cartpole", multi_threaded=True, timeout=30)

...

# start training in a separate thread
threading.Thread(target=trainer.train).start()

# run the simulation in the main thread
env.run()

import threading

# import the environment loader
from skrl.envs.torch import load_omniverse_isaacgym_env

# load environment
env = load_omniverse_isaacgym_env(multi_threaded=True, timeout=30)

...

# start training in a separate thread
threading.Thread(target=trainer.train).start()

# run the simulation in the main thread
env.run()

Run the main script passing the configuration as command line arguments. For example:

python main.py task=Cartpole

API

skrl.envs.torch.loaders.load_omniverse_isaacgym_env(task_name: str = '', omniisaacgymenvs_path: str = '', show_cfg: bool = True, multi_threaded: bool = False, timeout: int = 30)

Load an Omniverse Isaac Gym environment

Omniverse Isaac Gym benchmark environments: https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs

Parameters

task_name (str, optional) – The name of the task (default: “”). If not specified, the task name is taken from the command line argument (task=TASK_NAME). Command line argument has priority over function parameter if both are specified
omniisaacgymenvs_path (str, optional) – The path to the omniisaacgymenvs directory (default: “”). If empty, the path will obtained from omniisaacgymenvs package metadata
show_cfg (bool, optional) – Whether to print the configuration (default: True)
multi_threaded (bool, optional) – Whether to use multi-threaded environment (default: False)
timeout (int, optional) – Seconds to wait for data when queue is empty in multi-threaded environment (default: 30)

Raises

ValueError – The task name has not been defined, neither by the function parameter nor by the command line arguments
RuntimeError – The omniisaacgymenvs package is not installed or the path is wrong

Returns

Omniverse Isaac Gym environment

Return type

omni.isaac.gym.vec_env.vec_env_base.VecEnvBase or omni.isaac.gym.vec_env.vec_env_mt.VecEnvMT