---
task_categories:
- robotics
tags:
- humanoid
- loco-manipulation
- human-object-interaction
- motion-capture
- reinforcement-learning
pretty_name: "Weave: Whole-Body Dexterous Loco-Manipulation"
size_categories:
- 1K<n<10K
---

# Weave: Learning Whole-Body Dexterous Loco-Manipulation from Human-Object Interactions

Reference motion and physical rollouts for [Weave](https://arxiv.org/abs/2609.16683)
([Hugging Face page](https://huggingface.co/papers/2609.16683)).

- **Paper:** https://arxiv.org/abs/2609.16683
- **Project page:** https://xiaohu-art.github.io/Weave/
- **Code:** https://github.com/xiaohu-art/Weave

## Contents

| Path | What it is | Clips | Duration |
|---|---|---|---|
| `reference/{train,test}/` | Interaction-preserving reference motions retargeted to the robot | 9,474 | 23.2 h |
| `rollout/{train,test}/` | Trajectories the trained policy physically executed in simulation | 8,699 | 21.2 h |
| `objects/` | Interaction-object assets: mesh, USD, voxel SDF, surface points, shared BPS basis | 9 objects | — |

Robot: Unitree G1 with 29 actuated body DoFs and two Inspire hands with 12
actuated finger DoFs. Everything is recorded at 50 fps.

## Data format

Every file under `reference/` and `rollout/` is a compressed NumPy archive with
the same 15 keys. 
Clips are concatenated along a single flat frame axis.
`T` is the total frame
count and `N` the number of clips.

| Key | Shape | dtype | Meaning |
|---|---|---|---|
| `joint_pos` | (T, 53) | float32 | joint positions, rad |
| `joint_vel` | (T, 53) | float32 | joint velocities, rad/s |
| `body_pos_w` | (T, 54, 3) | float32 | body positions, m |
| `body_quat_w` | (T, 54, 4) | float32 | body orientations, **wxyz** |
| `body_lin_vel_w` | (T, 54, 3) | float32 | body linear velocities, m/s |
| `body_ang_vel_w` | (T, 54, 3) | float32 | body angular velocities, rad/s |
| `object_pos_w` | (T, 3) | float32 | object position, m |
| `object_quat_w` | (T, 4) | float32 | object orientation, **wxyz** |
| `object_lin_vel_w` | (T, 3) | float32 | object linear velocity, m/s |
| `object_ang_vel_w` | (T, 3) | float32 | object angular velocity, rad/s |
| `contact_label` | (T, 54) | float32 | per-body contact annotation, see below |
| `motion_lengths` | (N,) | int64 | frame count of each clip |
| `motion_names` | (N,) | unicode | clip identifiers |
| `object_names` | (N,) | unicode | object each clip interacts with |
| `fps` | (1,) | int64 | 50 for every file |


### `contact_label` differs between the two sets

In `reference/` the label is three-valued and carries both positive and
negative supervision:

| Value | Meaning |
|---|---|
| `1` | the body should be in contact with the object |
| `-1` | the body should **not** be in contact |
| `0` | unconstrained |

In `rollout/` the label is the measured contact state of the executed
trajectory, thresholded at 1 N, so it takes only `0` and `1`. Code that
consumes the reference labels with a `label != 0` mask will therefore treat
every non-contact frame of a rollout as unconstrained. Convert before mixing
the two sets.

### Joint and body ordering

The 53 joints and 54 bodies follow the Isaac Lab articulation order of the
G1 + Inspire URDF. Index 0 of the body axis is `pelvis`.

<details>
<summary>Body order (54)</summary>

```
pelvis, left_hip_pitch_link, right_hip_pitch_link, waist_yaw_link,
left_hip_roll_link, right_hip_roll_link, waist_roll_link, left_hip_yaw_link,
right_hip_yaw_link, torso_link, left_knee_link, right_knee_link,
left_shoulder_pitch_link, right_shoulder_pitch_link, left_ankle_pitch_link,
right_ankle_pitch_link, left_shoulder_roll_link, right_shoulder_roll_link,
left_ankle_roll_link, right_ankle_roll_link, left_shoulder_yaw_link,
right_shoulder_yaw_link, left_elbow_link, right_elbow_link,
left_wrist_roll_link, right_wrist_roll_link, left_wrist_pitch_link,
right_wrist_pitch_link, left_wrist_yaw_link, right_wrist_yaw_link,
L_index_proximal, L_middle_proximal, L_pinky_proximal, L_ring_proximal,
L_thumb_proximal_base, R_index_proximal, R_middle_proximal, R_pinky_proximal,
R_ring_proximal, R_thumb_proximal_base, L_index_intermediate,
L_middle_intermediate, L_pinky_intermediate, L_ring_intermediate,
L_thumb_proximal, R_index_intermediate, R_middle_intermediate,
R_pinky_intermediate, R_ring_intermediate, R_thumb_proximal,
L_thumb_intermediate, R_thumb_intermediate, L_thumb_distal, R_thumb_distal
```
</details>

<details>
<summary>Joint order (53)</summary>

```
left_hip_pitch_joint, right_hip_pitch_joint, waist_yaw_joint,
left_hip_roll_joint, right_hip_roll_joint, waist_roll_joint,
left_hip_yaw_joint, right_hip_yaw_joint, waist_pitch_joint, left_knee_joint,
right_knee_joint, left_shoulder_pitch_joint, right_shoulder_pitch_joint,
left_ankle_pitch_joint, right_ankle_pitch_joint, left_shoulder_roll_joint,
right_shoulder_roll_joint, left_ankle_roll_joint, right_ankle_roll_joint,
left_shoulder_yaw_joint, right_shoulder_yaw_joint, left_elbow_joint,
right_elbow_joint, left_wrist_roll_joint, right_wrist_roll_joint,
left_wrist_pitch_joint, right_wrist_pitch_joint, left_wrist_yaw_joint,
right_wrist_yaw_joint, L_index_proximal_joint, L_middle_proximal_joint,
L_pinky_proximal_joint, L_ring_proximal_joint, L_thumb_proximal_yaw_joint,
R_index_proximal_joint, R_middle_proximal_joint, R_pinky_proximal_joint,
R_ring_proximal_joint, R_thumb_proximal_yaw_joint, L_index_intermediate_joint,
L_middle_intermediate_joint, L_pinky_intermediate_joint,
L_ring_intermediate_joint, L_thumb_proximal_pitch_joint,
R_index_intermediate_joint, R_middle_intermediate_joint,
R_pinky_intermediate_joint, R_ring_intermediate_joint,
R_thumb_proximal_pitch_joint, L_thumb_intermediate_joint,
R_thumb_intermediate_joint, L_thumb_distal_joint, R_thumb_distal_joint
```
</details>

## Object assets

`objects/<name>/` holds the interaction object in several forms:

| File | What it is |
|---|---|
| `<name>.obj` | source triangle mesh |
| `<name>.usd` | Isaac Sim asset, convex-decomposition collider |
| `sdf_128.npz` | signed distance field on a 128³ grid |
| `surface.npy` | surface sample points used for hand-object distance terms |

`clothesstand/` additionally carries a `.usda`, the text form of the same USD.

`objects/geometry/bps_128.npy` is the shared basis point set behind the
BPS-SDF geometry descriptor; it is common to all objects.

## Loading

```python
import numpy as np
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    "appolyn/Weave",
    "rollout/test/woodchair_rollout.npz",
    repo_type="dataset",
)
data = np.load(path, allow_pickle=True)

lengths = data["motion_lengths"]
starts = np.concatenate([[0], np.cumsum(lengths)])

i = 0  # first clip
sl = slice(starts[i], starts[i + 1])
print(data["motion_names"][i], data["object_names"][i], lengths[i], "frames")
print(data["joint_pos"][sl].shape)      # (L, 53)
print(data["body_pos_w"][sl].shape)     # (L, 54, 3)
print(data["object_pos_w"][sl].shape)   # (L, 3)
```

The files are 140 MB to 1.3 GB each. `np.load` on an `.npz` is
lazy, so reading `motion_lengths` or a single field does not decompress the
rest.

## Citation

```bibtex
@misc{cao2026weave,
  title = {{Weave}: Learning Whole-Body Dexterous Loco-Manipulation
           from Human-Object Interactions},
  author = {Liu Cao and Xingze Wu and Jingzhi Cui and Botian Xu
            and Mingzhi Pei and Ruoqu Chen and Mengdi Xu},
  year = {2026},
  eprint = {2609.16683},
  archivePrefix = {arXiv},
  primaryClass = {cs.RO},
  url = {https://arxiv.org/abs/2609.16683}
}
```