| --- |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: hf_viewer/train.parquet |
| tags: |
| - lerobot |
| - robotics |
| - imitation-learning |
| - mujoco |
| - act |
| task_categories: |
| - robotics |
| pretty_name: Hepha ACT Dataset |
| --- |
| |
| # tmeynier/hepha_act_100_domain_randomized |
|
|
| LeRobot-style behavior-cloning dataset generated from the Hepha MuJoCo simulation. |
|
|
| ## Summary |
|
|
| - Robot type: `hepha_mujoco` |
| - Codebase version: `v3.0` |
| - Episodes: `100` |
| - Frames: `200341` |
| - FPS: `30` |
| - Joint normalization: `min_max_0_1` |
|
|
| ## Features |
|
|
| - `timestamp`: `float32` `[1]` |
| - `frame_index`: `int64` `[1]` |
| - `episode_index`: `int64` `[1]` |
| - `index`: `int64` `[1]` |
| - `task_index`: `int64` `[1]` |
| - `episode.drawer_index`: `int64` `[1]` |
| - `episode.cube_position`: `float32` `[3]` |
| - `episode.cube_quaternion`: `float32` `[4]` |
| - `observation.drawer_index`: `int64` `[1]` |
| - `observation.state`: `float32` `[15]` |
| - `action`: `float32` `[15]` |
| - `observation.state_raw`: `float32` `[15]` |
| - `action_raw`: `float32` `[15]` |
| - `observation.images.head_camera`: `video` `[3, 480, 640]` |
|
|
| ## Policy-Facing Columns |
|
|
| - `observation.state`: normalized robot joints in `[0, 1]` |
| - `observation.drawer_index`: selected drawer target for each frame |
| - `action`: normalized next-step robot joint targets in `[0, 1]` |
| - `observation.images.head_camera`: RGB video frames from the robot camera |
|
|
| ## Extra Columns |
|
|
| - `observation.state_raw`: raw joint-sensor readings (including configured sensor noise) |
| - `action_raw`: raw next-step MuJoCo joint positions |
| - `episode.drawer_index`: selected drawer index for each frame |
| - `episode.cube_position`: initial cube position for each frame |
| - `episode.cube_quaternion`: initial cube orientation for each frame |
|
|
| ## Notes |
|
|
| The dataset was produced by first computing the full IK episode, then resampling the robot joint trajectory at a constant normalized joint speed before saving frames/actions. |
| Training code appends the normalized drawer target `(drawer_index - 1) / 8` to the policy state when `observation.drawer_index` is present. |
|
|
| Joint limits and the normalization formula are stored in `meta/info.json`. |
|
|
