Datasetpublic

jacob3333/interactive-world-sim-rotate-t-data

IWS Rotate-T 90° Demonstrations (1k) 1,000 scripted expert demonstrations (+100 validation) of the rotate-the-T-90°-clockwise task on the bimanual ALOHA push-T MuJoCo environment, in the Interactive World Simulator (IWS) HDF5 format. Collected with scripts/data_collection/collect_rotate_t.py from the interactive_world_sim repo. The T spawns upright (θ = 0) and both arms rotate it ~90° clockwise with a closed-loop scripted policy (≤30° sub-rotations with angle feedback, so push… See the full description on the dataset page: https://huggingface.co/datasets/jacob3333/interactive-world-sim-rotate-t-data.

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Dataset Card

IWS Rotate-T 90° Demonstrations (1k)

1,000 scripted expert demonstrations (+100 validation) of the rotate-the-T-90°-clockwise task on the bimanual ALOHA push-T MuJoCo environment, in the Interactive World Simulator (IWS) HDF5 format. Collected with scripts/data_collection/collect_rotate_t.py from the interactive_world_sim repo.

The T spawns upright (θ = 0) and both arms rotate it ~90° clockwise with a closed-loop scripted policy (≤30° sub-rotations with angle feedback, so push slippage is absorbed). Every episode runs the post-reset stabilization (stabilize_t: the T falls from z = 0.07 and settles on the table before anything is recorded). Only demos that actually rotated 80–105° CW while staying flat are kept.

Splits and blocks

Split	Episodes	Block	Start condition
`train/`	`episode_0 … episode_199`	fixed	T pinned at (0, 0), arm at fixed reset home (`--no_settle`)
`train/`	`episode_200 … episode_999`	random	T at random XY in ±0.08 m, randomized arm ready pose (`settle_arms`)
`val/`	`episode_0 … episode_19`	fixed	same as fixed train block, disjoint seeds
`val/`	`episode_20 … episode_99`	random	same as random train block, disjoint seeds

Collection seeds (ep_seed = seed·10⁶ + trial): train fixed 11–12, train random 21–28, val fixed 31, val random 41 — disjoint from the earlier rotate_t / rotate_t_fixed datasets (seeds 0, 100) and from the tight-eval protocol (seed 7000).

Episode schema (HDF5)

Identical to the IWS world-model MuJoCo dataset — drop-in for both world-model training and BC:

action              (T, 4)            float32  bimanual EE-XY targets [Lx, Ly, Rx, Ry]
env_state           (T, 7)            float32  T-block pose (xyz + wxyz quat)
obs/ee_pos          (T, 2, 4, 4)      float32  EE poses (left, right)
obs/images/top_pov  (T, 128, 128, 3)  uint8    top-down RGB
obs/joint_pos       (T, 14)           float32  both arms' joint positions
robot_bases         (T, 2, 4, 4)      float32  world_T_base (left, right)

Episode length is variable (multiples of 60 control steps at 10 Hz — one sub-rotation each). videos/ inside each split holds a 128×128 mp4 preview per episode.

Download

bash

python scripts/download_data_hf.py --repo jacob3333/interactive-world-sim-rotate-t-data \
    --local_dir data/rotate_t_1k
# or
hf download jacob3333/interactive-world-sim-rotate-t-data --repo-type dataset \
    --local-dir data/rotate_t_1k

Point IWS training at it with dataset.dataset_dir=data/rotate_t_1k (the loader reads train/ and val/ subdirectories).