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Baseline 1 - Storm
Baseline 2 - Curobo
Baseline 3 - Ours, prioritization ablated
⭐ Ours
Recent advances in sampling-based motion planning algorithms for high DOF arms leverage GPUs to provide SOTA performance. While these algorithms can be used to control multiple arms jointly, this approach scales poorly. To address this, we extend the STORM motion planning algorithm that uses sampling-based model-predictive-control (MPC) to handle multiple roboots. Our algorithm distributes the computation among the arms, with each computing its own path, while sharing this information with the other arms. We demonstrate the empirical advantages of this approach over SOTA algorithms when operating with both static and dynamic obstacles.
BibTex Code Here
#REQUIREMENTS: ubuntu >= 20.04, git, conda, gitlfs, isaac sim and curobo requirements (gpu with 8 gm vram (4060 or higher), 32 gb ram, etc... We developed with 4060 for the most of the time even its below min specs)
pick a name for your environment (%env%)
- create a new env using: 'conda create env -b %env%' (and then 'conda activate %env%') # make sure python >= 3.10
- then install isaac sim using “pip” installation instructions the isaac sim env inside the conda env https://docs.isaacsim.omniverse.nvidia.com/latest/installation/install_python.html
(pip is the way worked best to me and the rest of process follows, but any other isaac sim installation should work,
look for the instructions start with: conda create -n env_isaacsim python=3.11 # use %env% conda activate env_isaacsim # use %env% and keep from there with their tutoial)
- clone our repo: Git clone https://github.com/Dan7171/rl_for_curobo.git
- cd rl_for_curobo/curobo # currently rl_for_curobo is a legacy name for our mpc-multi arm project (sorry for the inconvenience, this will be changed soon to "mpc-multi-arm" or similar in following versions)
- install curobo in your conda env:
- pip install -e . --no-build-isolation
- if fails, try: SETUPTOOLS_SCM_PRETEND_VERSION_FOR_NVIDIA_CUROBO=0.0.0+local pip install -e . --no-build-isolation
# more instructions and examples here: https://curobo.org/get_started/1_install_instructions.html
# note that you don't need to clone the repository like they require, because we already have it in our repository (don't do that: git clone https://github.com/NVlabs/curobo.git)
- cd .. # back to rl_for_curobo
pip install . # install rl_for_curobo module in yourconda env
# should see some log and then at the end "Successfully installed rl_for_curobo-0.1.0"
- git lfs install # if not yet done
- git lfs pull # very important so that all big files like robots meshes will be pulled properly
- run hello world example # todo