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Installation

  1. Install Isaac Sim 5.1.0 by downloading the latest release and unzip it to a desired location $ISAACSIM_PATH.
  2. Clone Isaac Lab and setup a conda environment:
    git clone git@github.com:isaac-sim/IsaacLab.git # SSH recommended
    conda create -n lab python=3.11
    conda activate lab
    cd IsaacLab
    ln -s $ISAACSIM_PATH _isaac_sim
    ./isaaclab.sh -c lab
    ./isaaclab.sh -i none # install without additional RL libraries
    
    # reactivate the environment
    conda deactivate
    conda activate lab
    echo $PYTHONPATH
    You should see the isaac-sim related dependencies are added to $PYTHONPATH.
  3. pip install -U torch torchvision tensordict torchrl==0.10
  4. Clone active_adaptation and checkout to branch v0.4.2
    git clone git@github.com:Agent-3154/active-adaptation.git
    cd active-adaptation
    git checkout v0.4.2
    pip install -e .
  5. Clone this repository
    git clone https://github.com/xiaohu-art/Track.git
    cd Track
    pip install -e .

Training & Evaluation

  1. Acquire training reference motion
  2. Train the reference motion tracking policy:
    cd active-adaptation/scripts
    python train_ppo.py \
        task=motion \
        algo=ppo_track \
        task.num_envs=<num envs> \
        task.name=<task name> \             # task name for wandb
        total_frames=<total frames> \       # Total number of frames to collect
        checkpoint_path=<checkpoint path>   # resume training from checkpoint
  3. Play the trained policy:
    cd active-adaptation/scripts
    python play.py \
        task=motion \
        algo=ppo_track \
        task.num_envs=<num envs> \
        checkpoint_path=<checkpoint path> \
        export_policy=<true or false>   # whether to export the policy to onnx