jonarriza96/cmr_data

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 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

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:

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:

  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:

  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.

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.