# Installation
1. Install Isaac Lab, see the [installation guide](https://isaac-sim.github.io/IsaacLab/v2.0.0/source/setup/installation/index.html).
**Note**: Currently HOVER has been tested with Isaac Lab versions 2.0.0. After you clone the Isaac Lab
repository, check out the specific tag before installation. Also note that the `rsl_rl`
package is renamed to `rsl_rl_lib` with the current `v2.0.0` tag of Isaac Lab, causing installation issues.
This will be fixed once a new tag is created on the Isaac Lab repo.
This error would not affect this repo, as we have our own customized `rsl_rl` package.
```bash
git fetch origin
git checkout v2.0.0
```
2. Define the following environment variable to specify the path to your IsaacLab installation:
```bash
# Set the ISAACLAB_PATH environment variable to point to your IsaacLab installation directory
export ISAACLAB_PATH=**Teacher Training:** | GPU | Num Iterations | Time per Iteration (s) | Training Time (h) | |-----------|----------------|------------------------|-------------------| | RTX 4090 | 100'000 | 0.84 | 23.3 | | RTX A6000 | 100'000 | 1.90 | 52.8 | | L40 | 100'000 | 1.61 | 44.6 | **Student Training:** | GPU | Num Iterations | Time per Iteration (s) | Training Time (h) | |-----------|----------------|------------------------|-------------------| | RTX 4090 | 10'000 | 0.097 | 0.27 | | RTX A6000 | 10'000 | 0.18 | 0.50 | | L40 | 10'000 | 0.176 | 0.49 | ## Generalist vs. Specialist Policy The codebase allows to train both generalist and specialist policies: - Generalist policies allow to track different command configurations (or modes) with a single policy, as shown in the [HOVER][hover_paper] paper. - Specialist policies only allow to track a specific command configuration with a single policy, as shown in the [OmniH2O][omnih2o_paper] paper. Per default the codebase trains a specialist policy in OmniH2O mode (tracking head and hand positions). ```py distill_mask_sparsity_randomization_enabled = False distill_mask_modes = {"omnih2o": DISTILL_MASK_MODES_ALL["omnih2o"]} ``` A specialist in a different mode can be trained by modifying the `distill_mask_modes` in the [config file](./neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/neural_wbc_env_cfg_h1.py#73). For an example to train a specialist that tracks the joint angles, root linear velocity and root yaw orientation use this: ```py distill_mask_sparsity_randomization_enabled = False distill_mask_modes = {"humanplus": DISTILL_MASK_MODES_ALL["humanplus"]} ``` A generalist can be trained by removing/commenting out the specialist mask modes in the [config file](./neural_wbc/isaac_lab_wrapper/neural_wbc/isaac_lab_wrapper/neural_wbc_env_cfg_h1.py#73), ie. ```py distill_mask_sparsity_randomization_enabled = False distill_mask_modes = DISTILL_MASK_MODES_ALL ``` In the current implementation, we hand picked four modes that are discussed in the original paper for proof of life purposes. The user is free to add more modes to the `DISTILL_MASK_MODES_ALL` dictionary to make the generalist policy more general. We recommend the user to turn off sparsity randomization as the currently implemented randomization strategy (as described in the paper) might lead to motion ambiguity. In both cases the same commands from above can be used to launch the training. # Testing ## Play Teacher Policy In the project's root directory, ```bash ${ISAACLAB_PATH:?}/isaaclab.sh -p scripts/rsl_rl/play.py \ --num_envs 10 \ --reference_motion_path neural_wbc/data/data/motions/stable_punch.pkl \ --teacher_policy.resume_path neural_wbc/data/data/policy/h1:teacher \ --teacher_policy.checkpoint model_
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| Stable Punch - Mujoco (left) & Real Robot (right) | Stable Wave - Mujoco (left) & Real Robot (right) |