Add remaining seven DexJoCo tasks with all native camera views
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
- README.md +31 -13
- bimanual_assembly_delta_n100/episodes.json +0 -0
- bimanual_assembly_delta_n100/images.json +0 -0
- bimanual_assembly_delta_n100/ph_finetune/normalization.npz +3 -0
- bimanual_assembly_delta_n100/ph_finetune/train.npz +3 -0
- bimanual_assembly_delta_n100/ph_pretrain/normalization.npz +3 -0
- bimanual_assembly_delta_n100/ph_pretrain/train.npz +3 -0
- bimanual_assembly_delta_n100/videos.zip +3 -0
- bimanual_hanoi_delta_n100/episodes.json +0 -0
- bimanual_hanoi_delta_n100/images.json +0 -0
- bimanual_hanoi_delta_n100/ph_finetune/normalization.npz +3 -0
- bimanual_hanoi_delta_n100/ph_finetune/train.npz +3 -0
- bimanual_hanoi_delta_n100/ph_pretrain/normalization.npz +3 -0
- bimanual_hanoi_delta_n100/ph_pretrain/train.npz +3 -0
- bimanual_hanoi_delta_n100/videos.zip +3 -0
- bimanual_microwave_cook_delta_n100/episodes.json +0 -0
- bimanual_microwave_cook_delta_n100/images.json +0 -0
- bimanual_microwave_cook_delta_n100/ph_finetune/normalization.npz +3 -0
- bimanual_microwave_cook_delta_n100/ph_finetune/train.npz +3 -0
- bimanual_microwave_cook_delta_n100/ph_pretrain/normalization.npz +3 -0
- bimanual_microwave_cook_delta_n100/ph_pretrain/train.npz +3 -0
- bimanual_microwave_cook_delta_n100/videos.zip +3 -0
- bimanual_photograph_delta_n100/episodes.json +0 -0
- bimanual_photograph_delta_n100/images.json +0 -0
- bimanual_photograph_delta_n100/ph_finetune/normalization.npz +3 -0
- bimanual_photograph_delta_n100/ph_finetune/train.npz +3 -0
- bimanual_photograph_delta_n100/ph_pretrain/normalization.npz +3 -0
- bimanual_photograph_delta_n100/ph_pretrain/train.npz +3 -0
- bimanual_photograph_delta_n100/videos.zip +3 -0
- bimanual_unlock_ipad_delta_n100/episodes.json +0 -0
- bimanual_unlock_ipad_delta_n100/images.json +0 -0
- bimanual_unlock_ipad_delta_n100/ph_finetune/normalization.npz +3 -0
- bimanual_unlock_ipad_delta_n100/ph_finetune/train.npz +3 -0
- bimanual_unlock_ipad_delta_n100/ph_pretrain/normalization.npz +3 -0
- bimanual_unlock_ipad_delta_n100/ph_pretrain/train.npz +3 -0
- bimanual_unlock_ipad_delta_n100/videos.zip +3 -0
- fold_glasses_delta_n100/episodes.json +0 -0
- fold_glasses_delta_n100/images.json +0 -0
- fold_glasses_delta_n100/ph_finetune/normalization.npz +3 -0
- fold_glasses_delta_n100/ph_finetune/train.npz +3 -0
- fold_glasses_delta_n100/ph_pretrain/normalization.npz +3 -0
- fold_glasses_delta_n100/ph_pretrain/train.npz +3 -0
- fold_glasses_delta_n100/videos.zip +3 -0
- manifest.json +303 -0
- water_plant_delta_n100/episodes.json +0 -0
- water_plant_delta_n100/images.json +0 -0
- water_plant_delta_n100/ph_finetune/normalization.npz +3 -0
- water_plant_delta_n100/ph_finetune/train.npz +3 -0
- water_plant_delta_n100/ph_pretrain/normalization.npz +3 -0
- water_plant_delta_n100/ph_pretrain/train.npz +3 -0
README.md
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@@ -10,16 +10,25 @@ tags:
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Processed image-and-state demonstrations. Action layout: world position delta (3),
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world relative SO(3) rotation vector (3), absolute Allegro joint targets (16).
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Deltas use the measured observation at the same timestep. Both states and actions
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are min-max normalized; BC and RL share the same normalization statistics.
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Observation version: `initial_object_pose_v1`.
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| Task | Episodes | Transitions |
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| pinch_tongs | 100 | 40469 |
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| pick_bucket | 100 | 43581 |
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| hammer_nail | 100 | 21563 |
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| click_mouse | 100 | 32702 |
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Each task contains `ph_pretrain/train.npz`, `ph_finetune/train.npz`, their
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`normalization.npz` files, and `episodes.json`. The manifest records SHA-256 hashes.
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These are synthetic end-of-demo success labels, not replay-verified success times
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for the original recordings. No model checkpoints are included.
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## RGB images (all
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The original MP4 files are preserved byte for byte, including their resolution.
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`images.json` maps each processed timestep to its exact source video frame;
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the same initial-prefix cut is used for states, delta actions, and images.
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Use `agent.dataset.dexjoco_image.DexJoCoImageDataset` (BC) or
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`DexJoCoImageQLearningDataset` (offline RL) from the accompanying checkout.
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The loader returns RGB uint8 `(B, history, 3 * cameras, height, width)` in
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`conditions["rgb"]` and normalized robot proprioception (first 23 state columns
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in `conditions["state"]`. Task/object privileged state is excluded by default.
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Frames are decoded on demand, with bounded caches and exact PTS-based seeking.
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Choose cameras and image size at training time; defaults are `front,wrist`
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download or offline image conversion is required.
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Choose the number of published episodes used for training with
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`max_n_episodes`; all
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## Array format
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| Key | Shape | Meaning |
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| --- | --- | --- |
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| `states` | `(T, obs_dim)` | Normalized observations, float32 |
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| `actions` | `(T,
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| `traj_lengths` | `(N,)` | Episode lengths, int64; sum equals T |
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| `rewards`, `terminals` | `(T,)` | Finetuning split only, float32 |
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`R_target = Exp(delta_rotvec) @ R_observed`. Hand targets stay absolute.
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The state and raw target quaternions use wxyz ordering.
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For `initial_object_pose_v1`, object pose features describe the initial object
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poses, matching the original recordings. They do not track moving objects.
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Processed image-and-state demonstrations. Action layout: world position delta (3),
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world relative SO(3) rotation vector (3), absolute Allegro joint targets (16).
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+
This is 22 values for single-arm tasks; bimanual tasks concatenate right then
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left arm blocks for 44 values, preserving each recorded hand's joint ordering.
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Deltas use the measured observation at the same timestep. Both states and actions
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are min-max normalized; BC and RL share the same normalization statistics.
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Observation version: `initial_object_pose_v1`.
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+
| Task | Episodes | Transitions | State dimension | Action dimension |
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| --- | ---: | ---: | ---: | ---: |
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| pinch_tongs | 100 | 40469 | 31 | 22 |
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| pick_bucket | 100 | 43581 | 38 | 22 |
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| hammer_nail | 100 | 21563 | 38 | 22 |
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| click_mouse | 100 | 32702 | 31 | 22 |
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| fold_glasses | 100 | 53878 | 38 | 22 |
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| water_plant | 100 | 27745 | 38 | 22 |
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| bimanual_assembly | 100 | 54630 | 61 | 44 |
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| bimanual_hanoi | 100 | 107764 | 50 | 44 |
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| bimanual_microwave_cook | 100 | 68503 | 61 | 44 |
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| bimanual_photograph | 100 | 34377 | 61 | 44 |
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| bimanual_unlock_ipad | 100 | 41042 | 61 | 44 |
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Each task contains `ph_pretrain/train.npz`, `ph_finetune/train.npz`, their
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`normalization.npz` files, and `episodes.json`. The manifest records SHA-256 hashes.
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These are synthetic end-of-demo success labels, not replay-verified success times
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for the original recordings. No model checkpoints are included.
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## RGB images (all native cameras)
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Single-arm episodes include `front`, `wrist`, `ego_left`, and `ego_right`.
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Bimanual episodes include `ego`, `wrist_left`, and `wrist_right`.
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The original MP4 files are preserved byte for byte, including their resolution.
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`images.json` maps each processed timestep to its exact source video frame;
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the same initial-prefix cut is used for states, delta actions, and images.
|
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Use `agent.dataset.dexjoco_image.DexJoCoImageDataset` (BC) or
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`DexJoCoImageQLearningDataset` (offline RL) from the accompanying checkout.
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The loader returns RGB uint8 `(B, history, 3 * cameras, height, width)` in
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+
`conditions["rgb"]` and normalized robot proprioception (first 23 state columns
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+
for single-arm tasks, first 46 for bimanual tasks)
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in `conditions["state"]`. Task/object privileged state is excluded by default.
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Frames are decoded on demand, with bounded caches and exact PTS-based seeking.
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+
Choose cameras and image size at training time; defaults are `front,wrist` for
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single-arm tasks, all three cameras for bimanual tasks, and 224 x 224 pixels.
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The loader reads the published videos directly; no source-dataset
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download or offline image conversion is required.
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Choose the number of published episodes used for training with
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`max_n_episodes`; all native camera views remain available.
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## Array format
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| Key | Shape | Meaning |
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| --- | --- | --- |
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| `states` | `(T, obs_dim)` | Normalized observations, float32 |
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+
| `actions` | `(T, action_dim)` | Normalized pose deltas and absolute hand targets, float32 |
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| 74 |
| `traj_lengths` | `(N,)` | Episode lengths, int64; sum equals T |
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| `rewards`, `terminals` | `(T,)` | Finetuning split only, float32 |
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| 76 |
|
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`R_target = Exp(delta_rotvec) @ R_observed`. Hand targets stay absolute.
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The state and raw target quaternions use wxyz ordering.
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Bimanual state begins `[right_pose7, left_pose7, right_hand16, left_hand16]`.
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Bimanual raw targets and `decode_actions` output instead use
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`[right_pose7, right_hand16, left_pose7, left_hand16]`.
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Before sending decoded targets to the simulator's policy-mode API, reorder
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them to `[right_pose7, left_pose7, right_hand16, left_hand16]`.
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+
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For `initial_object_pose_v1`, object pose features describe the initial object
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poses, matching the original recordings. They do not track moving objects.
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bimanual_assembly_delta_n100/episodes.json
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bimanual_assembly_delta_n100/images.json
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bimanual_assembly_delta_n100/ph_finetune/normalization.npz
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bimanual_hanoi_delta_n100/episodes.json
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bimanual_hanoi_delta_n100/images.json
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bimanual_hanoi_delta_n100/ph_finetune/normalization.npz
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bimanual_microwave_cook_delta_n100/episodes.json
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bimanual_microwave_cook_delta_n100/images.json
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bimanual_photograph_delta_n100/episodes.json
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bimanual_photograph_delta_n100/images.json
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bimanual_photograph_delta_n100/ph_finetune/normalization.npz
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