---
license: mit
task_categories:
  - robotics
tags:
  - robot-manipulation
  - mujoco
  - aloha
  - world-model
  - imitation-learning
pretty_name: IWS Rotate-T 90° Demonstrations (1k)
---

# 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](https://github.com/bingqingchen/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).