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isaacgym-utils

This repo contains wrappers and utilities for isaacgym

Supported version of Isaac Gym: 1.0rc2

Supported up_axis mode: z (UP_AXIS_Z in the Isaac Gym documentation)

Installation

Install IsaacGym

Install IsaacGym from Nvidia

This library supports the latest IsaacGym version - 1.0.preview2. It will not work with older versions.

Install isaacgym-utils

Install isaacgym-utils:

git clone git@github.com:iamlab-cmu/isaacgym-utils.git
pip install -e isaacgym-utils

By default, only "core" dependencies are installed. To install dependencies needed for optional isaagym-utils capabilities, modify the above pip install command to indicate the desired optional capability. The following commands will also install the core dependencies.

Reinforcement learning (RL):

pip install -e isaacgym-utils[rl]

Parallel IsaacGym instances via Ray:

pip install -e isaacgym-utils[ray]

Multiple capabilities can be specified:

pip install -e isaacgym-utils[ray,rl]

Or, install all capabilities:

pip install -e isaacgym-utils[all]

Running examples

Examples scripts can be found in isaacgym-utils/examples/. These scripts need to be run at the root level of isaacgym-utils:

cd isaacgym-utils
python examples/run_franka.py

Each example script has a corresponding config file in cfg/ that can be used to change object properties like friction.

Running with Ray

Ray is a fast and simple framework for building and running distributed applications.

Requires the [ray] or [all] installation of isaacgym-utils.

See isaacgym_utils/examples/franka_pick_block_ray.py for an example of running multiple isaacgym instances in parallel using Ray.

RL environment

Requires the [rl] or [all] installation of isaacgym-utils.

See isaacgym_utils/rl/vec_env.py for the abstract Vec Env base class that is used for RL. It contains definitions of methods that are expected to be overwritten by a child class for a specific RL environment.

See isaacgym_utils/rl/franka_vec_env.py for an example of an RL env with a Franka robot using joint control, variable impedance control, and hybrid force-position control.

See examples/run_franka_rl_vec_env.py for an example of running the RL environment, and refer to the corresponding config for changing various aspects of the environment (e.g. in the YAML config, the fields under franka.action determine what type of action space is used).

For new tasks and control schemes, you can make a new class that inherits GymVecEnv (or GymFrankaVecEnv if using the Franka) and overwrite the appropriate methods.

Loading external objects

To load external meshes, the meshes need to be wrapped in an URDF file. See assets/ycb for some examples. The script scripts/mesh_to_urdf.py can help make these URDFs, but using it is not necessary. Then, they can be loaded via GymURDFAsset. See GymFrankaBlockVecEnv._setup_single_env_gen in isaacgym_utils/rl/franka_vec_env.py for an example.

Tensor API

To use IsaacGym's Tensor API, set scene->gym->use_gpu_pipeline: True in the yaml configs.

This switches isaacgym-utils' API to use the Tensor API backend, and you can access the tensors directly using scene.tensors.

To directly write values into writable tensors (see IsaacGym docs for more details), instead of relying on isaacgym-utils' internal implementations, you should:

  1. Write to a tensor in scene.tensors
  2. Call scene.register_actor_tensor_to_update to ensure that the writes are committed during the next simulation step.

Things to Note

Citation

If you use isaacgym-utils in a publication, please consider citing the repo:

@misc{isaacgym-utils,
title = {IsaacGym Utilities},
year = {2021},
note = {Developed by the CMU Intelligent Autonomous Manipulation Lab},
url={https://github.com/iamlab-cmu/isaacgym-utils},
author = {Liang, Jacky},
}