| --- |
| pretty_name: DexJoCo Delta Actions for DICE-RL |
| tags: |
| - robotics |
| - reinforcement-learning |
| - dexjoco |
| - dice-rl |
| --- |
| # DexJoCo delta-action data for DICE-RL |
|
|
| Processed image-and-state demonstrations. Action layout: world position delta (3), |
| world relative SO(3) rotation vector (3), absolute Allegro joint targets (16). |
| This is 22 values for single-arm tasks; bimanual tasks concatenate right then |
| left arm blocks for 44 values, preserving each recorded hand's joint ordering. |
| Deltas use the measured observation at the same timestep. Both states and actions |
| are min-max normalized; BC and RL share the same normalization statistics. |
| Observation version: `initial_object_pose_v1`. |
|
|
| | Task | Episodes | Transitions | State dimension | Action dimension | |
| | --- | ---: | ---: | ---: | ---: | |
| | pinch_tongs | 100 | 40469 | 31 | 22 | |
| | pick_bucket | 100 | 43581 | 38 | 22 | |
| | hammer_nail | 100 | 21563 | 38 | 22 | |
| | click_mouse | 100 | 32702 | 31 | 22 | |
| | fold_glasses | 100 | 53878 | 38 | 22 | |
| | water_plant | 100 | 27745 | 38 | 22 | |
| | bimanual_assembly | 100 | 54630 | 61 | 44 | |
| | bimanual_hanoi | 100 | 107764 | 50 | 44 | |
| | bimanual_microwave_cook | 100 | 68503 | 61 | 44 | |
| | bimanual_photograph | 100 | 34377 | 61 | 44 | |
| | bimanual_unlock_ipad | 100 | 41042 | 61 | 44 | |
| |
| Each task contains `ph_pretrain/train.npz`, `ph_finetune/train.npz`, their |
| `normalization.npz` files, and `episodes.json`. The manifest records SHA-256 hashes. |
| RL rewards and terminals are 1 only at each retained demonstration's final frame. |
| These are synthetic end-of-demo success labels, not replay-verified success times |
| for the original recordings. No model checkpoints are included. |
| |
| ## RGB images (all native cameras) |
| |
| Single-arm episodes include `front`, `wrist`, `ego_left`, and `ego_right`. |
| Bimanual episodes include `ego`, `wrist_left`, and `wrist_right`. |
| The original MP4 files are preserved byte for byte, including their resolution. |
| `images.json` maps each processed timestep to its exact source video frame; |
| the same initial-prefix cut is used for states, delta actions, and images. |
| Each task stores videos under `videos/episode_XXXX/<camera>.mp4`. |
| On the Hub, `videos.zip` bundles every camera video for a task. The download |
| script automatically extracts and verifies them, preserving the original bytes. |
|
|
| Use `agent.dataset.dexjoco_image.DexJoCoImageDataset` (BC) or |
| `DexJoCoImageQLearningDataset` (offline RL) from the accompanying checkout. |
| The loader returns RGB uint8 `(B, history, 3 * cameras, height, width)` in |
| `conditions["rgb"]` and normalized robot proprioception (first 23 state columns |
| for single-arm tasks, first 46 for bimanual tasks) |
| in `conditions["state"]`. Task/object privileged state is excluded by default. |
| Frames are decoded on demand, with bounded caches and exact PTS-based seeking. |
| Choose cameras and image size at training time; defaults are `front,wrist` for |
| single-arm tasks, all three cameras for bimanual tasks, and 224 x 224 pixels. |
| The loader reads the published videos directly; no source-dataset |
| download or offline image conversion is required. |
|
|
| Choose the number of published episodes used for training with |
| `max_n_episodes`; all native camera views remain available. |
|
|
|
|
| ## Array format |
|
|
| Load NPZ files with `numpy.load(path, allow_pickle=False)`. |
|
|
| | Key | Shape | Meaning | |
| | --- | --- | --- | |
| | `states` | `(T, obs_dim)` | Normalized observations, float32 | |
| | `actions` | `(T, action_dim)` | Normalized pose deltas and absolute hand targets, float32 | |
| | `traj_lengths` | `(N,)` | Episode lengths, int64; sum equals T | |
| | `rewards`, `terminals` | `(T,)` | Finetuning split only, float32 | |
|
|
| `normalization.npz` stores `obs_min`, `obs_max`, `action_min`, `action_max`, |
| `action_mode`, `normalized`, and `observation_version`. |
| Normalization is `2 * (x - minimum) / (maximum - minimum + 1e-6) - 1`. |
| Unnormalize actions before decoding them with the latest measured robot pose: |
| `target_xyz = observed_xyz + delta_xyz` and |
| `R_target = Exp(delta_rotvec) @ R_observed`. Hand targets stay absolute. |
| The state and raw target quaternions use wxyz ordering. |
|
|
| Bimanual state begins `[right_pose7, left_pose7, right_hand16, left_hand16]`. |
| Bimanual raw targets and `decode_actions` output instead use |
| `[right_pose7, right_hand16, left_pose7, left_hand16]`. |
| Before sending decoded targets to the simulator's policy-mode API, reorder |
| them to `[right_pose7, left_pose7, right_hand16, left_hand16]`. |
|
|
| For `initial_object_pose_v1`, object pose features describe the initial object |
| poses, matching the original recordings. They do not track moving objects. |
|
|
| Use the accompanying DICE-RL checkout's `bash script/download_dexjoco_hf.sh`. |
| See its `README_DEXJOCO.md` for installation and action decoding. |
| Source recordings: [DexJoCo](https://huggingface.co/datasets/DexJoCo/DexJoCo-Datasets-Raw). |
|
|
