README.md

metadata

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 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.