metadata
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 (Hugging Face page).
- 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.
Body order (54)
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
Joint order (53)
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
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
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
@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}
}