SSIO (yellow), trained self-supervised on this dataset's 1 h of data, against SuperOdom ground truth (red), at 8× speed.
Real-robot recordings of a Unitree G1 humanoid: the pelvis IMU, all 29 joint states, and
ground-truth pose and velocity from SuperOdom LiDAR-inertial odometry. It is the humanoid
benchmark of **[SSIO](https://ssio-imu.github.io)**, which trains inertial odometry from
the robot's own unlabeled IMU stream.
## Dataset Structure
Each clip is one folder, named `___`, with the
motions navigation, walking, combo, dance, kick, sprint, and general (`humanoid`).
| Split | File | Clips | Frames | Duration (hours) |
| --- | --- | ---: | ---: | ---: |
| train | `splits/train.txt` | 31 | 799,388 | 1.11 |
| test | `splits/test.txt` | 1 | 43,991 | 0.06 |
| **Total** | | **32** | **843,379** | **1.17** |
The splits are the SSIO paper's. Validation uses the 31 training clips with
non-overlapping windows, so it serves model selection only. The test clip,
`10871_humanoid_20260520_223451`, comes from the same session as 8 training clips.
## Data Format
All arrays are plain NumPy files (`allow_pickle=False`), share the timestamps in
`time_ns.npy`, and are finite.
- **`imus.npy`** `(P, T, 9)` float16: per body part, `acc` [0:3] (m/s², specific force,
not bias-corrected), `gyro` [3:6] (rad/s), and ground-truth body-frame velocity `gt_vel`
[6:9] (m/s). Cast to float32 before use.
- **`pose.npy`** `(P, T, 7)` float32: ground-truth world pose `[tx, ty, tz, qx, qy, qz, qw]`;
the quaternion is body to world (`R_WB`), xyzw order.
- **`time_ns.npy`** `(T,)` int64: timestamps in ns at a nominal 200 Hz. The stream has gaps
(up to 210 ms), so integrate with the real `dt`.
- **`joints.npy`** `(T, 29, 3)` float32: joint position `q` (rad), velocity `dq` (rad/s),
and estimated torque `tau_est` (N·m), with names and units in `joints.json`.
- **`spec.json`**: format version, length `T`, and the part names.
`P = 14` body parts, in the order of `spec.json["names"]`: `pelvis, head, left_upper_arm,
left_forearm, left_hand, right_upper_arm, right_forearm, right_hand, left_upper_leg,
left_lower_leg, left_foot, right_upper_leg, right_lower_leg, right_foot`. Part 0,
`pelvis`, is the physical G1 IMU, re-expressed in the pelvis frame; the other 13 are
IMU-format streams derived from the robot's body motion. Body frames report in the
part's own frame; the world frame is z-up, with `R_WB @ acc ≈ [0, 0, +g]` at rest.
## Quick Usage
```bash
pip install huggingface_hub numpy
hf download Open-IO/humanoid-g1-odometry --repo-type dataset --local-dir humanoid_g1
```
```python
import json
from pathlib import Path
import numpy as np
clip = Path("humanoid_g1") / (Path("humanoid_g1/splits/train.txt").read_text().split()[0])
k = json.loads((clip / "spec.json").read_text())["names"].index("pelvis")
imus = np.load(clip / "imus.npy")[k].astype(np.float32) # (T, 9)
pose = np.load(clip / "pose.npy")[k] # (T, 7)
t_ns = np.load(clip / "time_ns.npy") # (T,)
joints = np.load(clip / "joints.npy") # (T, 29, 3)
acc, gyro, gt_vel_body = imus[:, 0:3], imus[:, 3:6], imus[:, 6:9]
```
To train or evaluate with the [SSIO code](https://github.com/amazon-far/SSIO), download
into `$DATA_ROOT/humanoid_g1` and use the `HumanoidOdom_*` dataset configs; the
`HumanoidOdom_Joints_*` configs also read `joints.npy`.
## Citation
https://ssio-imu.github.io
```bibtex
@misc{qiu2026ssioselfsupervisedinertialodometry,
title={SSIO: Self-Supervised Inertial Odometry},
author={Yuheng Qiu and Yutian Chen and Carlton Cort and Weison Huang and Shibo Zhao and Pieter Abbeel and Rocky Duan and Koushil Sreenath and C. Karen Liu and Carmelo Sferrazza and Junyi Geng and Sebastian Scherer and Guanya Shi},
year={2026},
eprint={2610.25988},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2610.25988},
}
```