| --- |
| 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). |
|
|
