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 — DDPM diffusion
• Cloth-splatters/dexgarmentlab-dual-dynamics-gps-flow — flow matching
• Cloth-splatters/dexgarmentlab-dual-dynamics-gns — Graph Network Simulator