SEW-TWIST G1 Teleoperation Dataset

This dataset contains offline teleoperation trajectories for the Unitree G1 humanoid robot generated using the SEW-MIMIC controller [1] and LaFAN1 BVH motion capture data [2].

The dataset was generated by replaying BVH motion capture sequences through a MuJoCo simulation of the G1 robot and logging the resulting robot state trajectories in a format compatible with TWIST-style imitation learning pipelines [3].

Each trajectory is stored as a .pkl file containing joint states, root pose, and body positions for each simulation frame.

---

Dataset Structure

dataset/
│
├── *.pkl              # Motion trajectories
├── metadata.csv       # Optional metadata per sequence
└── README.md

Each .pkl file contains a single trajectory sequence recorded from BVH playback.

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Data Format

Each .pkl file contains a Python dictionary with the following structure:

{
    "fps": float,
    "root_pos": np.ndarray,
    "root_rot": np.ndarray,
    "dof_pos": np.ndarray,
    "local_body_pos": np.ndarray,
    "link_body_list": list[str]
}

---

Fields

fps

float

Frames per second of the recorded trajectory.

---

root_pos

shape: (T, 3)
dtype: float32

World-space position of the robot root (pelvis mocap frame).

[x, y, z]

Units: meters

---

root_rot

shape: (T, 4)
dtype: float32

Root orientation quaternion in (x, y, z, w) format.

This is converted from MuJoCo's internal (w, x, y, z) ordering during logging.

---

dof_pos

shape: (T, 21)
dtype: float32

Joint configuration vector for the robot.

The DOF vector is concatenated as:

[left_leg, right_leg, torso, left_arm, right_arm]

DOF Breakdown

Body Part	DOF
Left Leg	6
Right Leg	6
Torso	3
Left Arm	4
Right Arm	4

Total: 23 DOF

---

local_body_pos

shape: (T, N, 3)
dtype: float32

Local body positions for each link relative to the root frame.

Positions are computed as:

local = R_root^T * (x_world - root_pos)

Where

• R_root is the root rotation matrix
• x_world is the world position of the body

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link_body_list

list[str] length = N

Names of the robot bodies corresponding to the local_body_pos array.

Example entries include pelvis, hip joints, knees, ankles, torso, shoulders, elbows, wrists, and hands.

---

Example Usage

import pickle

with open("trajectory.pkl", "rb") as f:
    data = pickle.load(f)

print(data.keys())

Output:

dict_keys([
    'fps',
    'root_pos',
    'root_rot',
    'dof_pos',
    'local_body_pos',
    'link_body_list'
])

---

Intended Use

This dataset can be used for:

• humanoid motion imitation learning
• motion retargeting research
• policy learning from motion capture
• trajectory prediction
• humanoid control benchmarking

---

Citation

This dataset is released as part of the SEW-MIMIC project

@misc{sew_mimic,
  title={A Closed-Form Geometric Retargeting Solver for Upper Body Humanoid Robot Teleoperation},
  author={Kong, Chuizheng and Cho, Yunho and Jung, Wonsuhk and others},
  year={2026},
  note={Project website: https://sew-mimic.com/}
}

---

Acknowledgements

• LaFAN1 Motion Capture Dataset
• MuJoCo Physics Engine
• SEW Geometric Teleoperation Framework

References

[1] A Closed-Form Geometric Retargeting Solver for Upper Body Humanoid Robot Teleoperation,
Project website: https://sew-mimic.com/ arXiv preprint arXiv:2602.01632, 2026

[2] LaFAN1 Motion Capture Dataset
Ubisoft La Forge.
https://github.com/ubisoft/ubisoft-laforge-animation-dataset

[3] Y. Ze, Z. Chen, J. P. Araújo, Z. Cao, X. B. Peng, J. Wu, and C. K. Liu,
"TWIST: Teleoperated Whole-Body Imitation System,"
Project website: https://yanjieze.com/TWIST/

Data Sources and Licensing

This dataset is generated from the LaFAN1 Motion Capture Dataset provided by Ubisoft La Forge.

The original dataset is licensed under:

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

See the original repository for details:
https://github.com/ubisoft/ubisoft-laforge-animation-dataset

Users of this dataset must comply with the license terms of the original dataset.