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If you have a working Isaac Lab environment, you can directly install TacEx.
Otherwise, you need to install Isaac Sim 4.5 and Isaac Lab 2.1. Below is a quick summary, but here is the full installation guide.

Quick summary for Installing Isaac Sim and Isaac Lab for Ubuntu 22.04

[!note] To install Isaac Sim for Ubuntu 20.04 follow the binary installation guide.

Isaac Sim - Linux pip installation

# create virtual environment
conda create -n env_isaaclab python=3.10
conda activate env_isaaclab
# install cuda-enabled pytorch
pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu118
pip install --upgrade pip
# install isaac sim packages
pip install 'isaacsim[all,extscache]==4.5.0' --extra-index-url https://pypi.nvidia.com

verify that the Isaac Sim installation works by calling isaacsim in the terminal

Isaac Lab

# install dependencies via apt (Ubuntu)
sudo apt install cmake build-essential
git clone https://github.com/isaac-sim/IsaacLab
cd IsaacLab
# use Isaac Lab version 2.1
git checkout 90b79bb2d44feb8d833f260f2bf37da3487180ba
# activate the Isaac Sim python env
conda activate env_isaaclab
# install isaaclab extensions (with --editable flag)
./isaaclab.sh --install # or "./isaaclab.sh -i"

To verify the Isaac Lab Installation:

conda activate env_isaaclab
python scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Ant-v0 --headless

Installing TacEx [Core]

0. If you haven't already done so, clone the repository and its submodules:

# Need it for the USD assets
git lfs install
git clone --recurse-submodules https://github.com/DH-Ng/TacEx.git
cd TacEx

1. Activate the Isaac Env

conda activate env_isaaclab

2. Install the core packages of TacEx

# Script will pip install core TacEx packages with --editable flag)
./tacex.sh -i

You can install the extensions one by one via e.g. python -m pip install -e source/tacex_uipc

3. Verify that TacEx works by running an example:

python ./scripts/demos/tactile_sim_approaches/check_taxim_sim.py --debug_vis

And here is an RL example:

python ./scripts/reinforcement_learning/skrl/train.py --task TacEx-Ball-Rolling-Tactile-RGB-v0 --num_envs 512 --enable_cameras

You can view the sensor output in the IsaacLab Tab: Scene Debug Visualization > Observations > sensor_output

Installing TacEx [UIPC]

The tacex_uipc package is responsible for the UIPC simulation in TacEx.

1. Install the libuipc dependencies:

  • If not installed yet, install Vcpkg
mkdir ~/Toolchain
cd ~/Toolchain
git clone https://github.com/microsoft/vcpkg.git
cd vcpkg
./bootstrap-vcpkg.sh -disableMetrics
  • Set the System Environment Variable CMAKE_TOOLCHAIN_FILE to let CMake detect Vcpkg. If you installed it like above, you can do this:
# Write in ~/.bashrc
export CMAKE_TOOLCHAIN_FILE="$HOME/Toolchain/vcpkg/scripts/buildsystems/vcpkg.cmake"
  • We also need CMake 3.26, GCC 11.4 and Cuda 12.4 to build libuipc. Install this into the Isaac Sim python env:
# Inside the root dir of TacEx repo
conda activate env_isaaclab
conda env update -n env_isaaclab --file ./source/tacex_uipc/libuipc/conda/env.yaml

If Cuda 12.4 does not work for, try updating your Nvidia drivers or try to use an older Cuda version by adjusting the env.yaml file (e.g. Cuda 12.2).

2. Install tacex_uipc

# This also builds `libuipc` and pip installs the python bindings.
conda activate env_isaaclab
pip install -e source/tacex_uipc -v

You can also install all TacEx packages with ./tacex.sh -i all.

3. Verify that the tacex_uipc works by running an example:

python ./scripts/benchmarking/tactile_sim_performance/run_ball_rolling_experiment.py --num_envs 1 --debug_vis --env uipc

Code formatting

There is a pre-commit template to automatically format your code. To install pre-commit:

pip install pre-commit

Then you can run pre-commit with:

pre-commit run --all-files

Setup Vscode

Full setup Guide can be found here.

In a nutshell:

  1. Run VSCode Tasks, by pressing Ctrl+Shift+Pand selecting Tasks: Run Task
  2. Select setup_python_env in the drop down menu.

Now you should have

  • .vscode/launch.json, which contains the launch configurations for debugging python code.
  • .vscode/settings.json, which contains the settings for the python interpreter and the python environment.