---
license: mit
task_categories:
- robotics
tags:
- cloth
- deformable-objects
- garment
- bimanual
---

# dexgarmentlab-dual-meshes-clean

Two-hand DexGarmentLab garment manipulation demos with full mesh state,
point-cloud observations, and per-hand gripper trajectories.

- **File:** `dexgarmentlab_dual_gps2048_clean.h5` (1.4 GB)
- **239 garments** — 136 Top, 77 Pants, 26 Dress (14 fine classes)
- **894 trajectories**, **28 673 steps**, 12-59 steps per trajectory
- **1637-2048 vertices**, <=12 102 bidirectional edges per mesh
- **Tasks:** Fold (400 trajectories), Fling (249), Hang (200), Store (45)

## Split

The `training` / `validation` split is **baked into the file** at the top
level and is **by garment**: 215 training cloths / 24 validation cloths, zero
overlap. Every trajectory of a validation garment is held out, so this is a
held-out-*object* protocol rather than the held-out-trajectory protocol the
predecessor dataset forced. Loaders honour the groups directly and ignore
`val_trajectory_ratio`.

## Two hands

644 of 894 trajectories move both hands; the rest move one and park the other.
Per-hand truth:

- `gripper_pos_2h` — `(2, 3)` per step, both end-effector positions
- `actuated_vertices_2h` — `(2, V)` boolean, which vertices each hand holds
- `hand_active` — trajectory attribute, `(2,)` boolean

The file also carries legacy single-gripper `gripper_pos` `(3,)` /
`actuated_vertices` `(V,)` for backwards compatibility. **On a two-handed
trajectory these are lossy** and should not be used for training: the legacy
point is a *virtual* gripper sitting between the hands, measured ~14 cm from
the vertices it nominally drives (the real per-hand positions are 3-4 cm
away), and the legacy mask covers only ~85 of the ~153 vertices the two hands
actually hold.

## `-clean`: training only

Point clouds have had scene geometry (the robot arm) removed using the
ground-truth cloth mesh — points further than `reject_m = 0.05` from the
frame's own mesh are dropped. That segmentation uses information no estimator
has at inference time, so this file is **correct for training and wrong for
evaluating an observation model**. For evaluation use an uncleaned recording
with an outlier-robust likelihood (the particle filter's
`obs_trim_fraction`).

## Structure

```
{training,validation}/<cloth_key>/
    rest_positions      (V, 3) float32
    edges               (E, 2) int32
    faces               (F, 3) int32
    trajectory_<i>/
        actuated_vertices     (V,)    bool   # legacy, merged — see above
        actuated_vertices_2h  (2, V)  bool
        @hand_active          (2,)    bool
        @task, @demo, @n_steps, @source
        step_<nnnn>/
            positions      (V, 3) float32
            gripper_pos    (3,)   float32   # legacy, virtual — see above
            gripper_pos_2h (2, 3) float32
            pointclouds/   <= 2048 points, one camera
```

## Models trained on this dataset

- [Cloth-splatters/dexgarmentlab-dual-dynamics-gps](https://huggingface.co/Cloth-splatters/dexgarmentlab-dual-dynamics-gps) — DDPM diffusion
- [Cloth-splatters/dexgarmentlab-dual-dynamics-gps-flow](https://huggingface.co/Cloth-splatters/dexgarmentlab-dual-dynamics-gps-flow) — flow matching
- [Cloth-splatters/dexgarmentlab-dual-dynamics-gns](https://huggingface.co/Cloth-splatters/dexgarmentlab-dual-dynamics-gns) — Graph Network Simulator