| --- |
| license: unknown |
| pretty_name: CMR Retargeted Motions |
| tags: |
| - robotics |
| - humanoid |
| - motion-retargeting |
| - reinforcement-learning |
| - mujoco |
| size_categories: |
| - n<1K |
| --- |
| |
| # CMR Retargeted Motions |
|
|
| Motions retargeted to the Unitree G1 (29 DoF) humanoid and exported to the RL format the holosoma |
| stack consumes. This dataset is compliant with the holosoma motion-retargeting RL training pipeline. |
|
|
| The files are produced by the [`sqp_retargeting`](https://github.com/jonarriza96/sqp_retargeting) |
| repo (`export/convert_data_format_mj.py`), which replays already-retargeted `qpos` through MuJoCo |
| forward kinematics and records per-body world kinematics. Each timestamped run folder holds one |
| subfolder per suite (`robot_only_omomo/`, `robot_object_omomo/`, `robot_terrain/`), each with one |
| compressed `.npz` per clip, plus the run's `comparison.md`/`comparison.json`. |
|
|
| ## Download |
|
|
| ```bash |
| hf download jonarriza96/cmr_data --repo-type dataset --local-dir ./data |
| ``` |
|
|
| This downloads the run folders into `data/`, skipping files already present. The dataset is |
| **private**, so first `pip install huggingface_hub`, get access on Hugging Face, and authenticate once: |
|
|
| ```bash |
| hf auth login # token from https://huggingface.co/settings/tokens |
| ``` |
|
|
| ## Train |
|
|
| Run the RL training with the corresponding holosoma command, pointing `motion_dir` at the absolute |
| path of the downloaded suite folder: |
|
|
| - `robot-only`: |
|
|
| ```bash |
| python src/holosoma/holosoma/train_agent.py \ |
| exp:g1-29dof-wbt logger:wandb \ |
| --command.setup_terms.motion_command.params.motion_config.motion_dir="<abs>/data/102317_170726/robot_only_omomo" |
| ``` |
|
|
| - `robot-object`: |
|
|
| ```bash |
| python src/holosoma/holosoma/train_agent.py \ |
| exp:g1-29dof-wbt-w-object logger:wandb \ |
| --command.setup_terms.motion_command.params.motion_config.motion_dir="<abs>/data/102317_170726/robot_object_omomo" |
| ``` |
|
|
| ## File format |
|
|
| Each `*.npz` (robot-only clip) contains: |
|
|
| | key | shape | dtype | meaning | |
| |-----|-------|-------|---------| |
| | `fps` | `(1,)` | int64 | output frame rate | |
| | `joint_pos` | `(T, 36)` | float64 | generalized position: 3 base pos + 4 base quat (wxyz) + 29 DoF | |
| | `joint_vel` | `(T, 35)` | float64 | generalized velocity: 3 base lin + 3 base ang + 29 DoF | |
| | `body_pos_w` | `(T, nbody, 3)` | float64 | per-body world position | |
| | `body_quat_w` | `(T, nbody, 4)` | float64 | per-body world orientation (wxyz) | |
| | `body_lin_vel_w` | `(T, nbody, 3)` | float64 | per-body world linear velocity | |
| | `body_ang_vel_w` | `(T, nbody, 3)` | float64 | per-body world angular velocity | |
| | `joint_names` | `(29,)` | str | actuated joint names, in `joint_pos`/`joint_vel` order | |
| | `body_names` | `(nbody,)` | str | MuJoCo body names, in `body_*_w` order | |
|
|
| Object-interaction clips additionally carry `object_pos_w (T,3)`, `object_quat_w (T,4)`, |
| `object_lin_vel_w (T,3)` and `object_ang_vel_w (T,3)`; for those the object columns are stripped from |
| `joint_pos`/`joint_vel`. |
|
|
| ```python |
| import numpy as np |
| d = np.load("sub3_largebox_003_mj_fps50.npz", allow_pickle=True) |
| joint_pos = d["joint_pos"] # (T, 36) |
| ``` |
|
|
| ## License |
|
|
| `unknown` — set this before publishing. These motions are retargeted from upstream sources (e.g. |
| LAFAN1, OMOMO); the licenses of those datasets govern redistribution. |
|
|
