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Publish processed DexJoCo delta-action datasets

Browse files
README.md ADDED
@@ -0,0 +1,54 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ pretty_name: DexJoCo Delta Actions for DICE-RL
3
+ tags:
4
+ - robotics
5
+ - reinforcement-learning
6
+ - dexjoco
7
+ - dice-rl
8
+ ---
9
+ # DexJoCo delta-action data for DICE-RL
10
+
11
+ Processed state-based demonstrations. Action layout: world position delta (3),
12
+ world relative SO(3) rotation vector (3), absolute Allegro joint targets (16).
13
+ Deltas use the measured observation at the same timestep. Both states and actions
14
+ are min-max normalized; BC and RL share the same normalization statistics.
15
+ Observation version: `initial_object_pose_v1`.
16
+
17
+ | Task | Episodes | Transitions |
18
+ | --- | ---: | ---: |
19
+ | pinch_tongs | 50 | 19885 |
20
+ | pick_bucket | 50 | 22077 |
21
+ | hammer_nail | 50 | 11008 |
22
+ | click_mouse | 50 | 17120 |
23
+
24
+ Each task contains `ph_pretrain/train.npz`, `ph_finetune/train.npz`, their
25
+ `normalization.npz` files, and `episodes.json`. The manifest records SHA-256 hashes.
26
+ RL rewards and terminals are 1 only at each retained demonstration's final frame.
27
+ These are synthetic end-of-demo success labels, not replay-verified success times
28
+ for the original recordings. No raw videos or model checkpoints are included.
29
+
30
+ ## Array format
31
+
32
+ Load NPZ files with `numpy.load(path, allow_pickle=False)`.
33
+
34
+ | Key | Shape | Meaning |
35
+ | --- | --- | --- |
36
+ | `states` | `(T, obs_dim)` | Normalized observations, float32 |
37
+ | `actions` | `(T, 22)` | Normalized pose deltas and absolute hand targets, float32 |
38
+ | `traj_lengths` | `(N,)` | Episode lengths, int64; sum equals T |
39
+ | `rewards`, `terminals` | `(T,)` | Finetuning split only, float32 |
40
+
41
+ `normalization.npz` stores `obs_min`, `obs_max`, `action_min`, `action_max`,
42
+ `action_mode`, `normalized`, and `observation_version`.
43
+ Normalization is `2 * (x - minimum) / (maximum - minimum + 1e-6) - 1`.
44
+ Unnormalize actions before decoding them with the latest measured robot pose:
45
+ `target_xyz = observed_xyz + delta_xyz` and
46
+ `R_target = Exp(delta_rotvec) @ R_observed`. Hand targets stay absolute.
47
+ The state and raw target quaternions use wxyz ordering.
48
+
49
+ For `initial_object_pose_v1`, object pose features describe the initial object
50
+ poses, matching the original recordings. They do not track moving objects.
51
+
52
+ Use the accompanying DICE-RL checkout's `bash script/download_dexjoco_hf.sh`.
53
+ See its `README_DEXJOCO.md` for installation and action decoding.
54
+ Source recordings: [DexJoCo](https://huggingface.co/datasets/DexJoCo/DexJoCo-Datasets-Raw).
click_mouse_delta_n50/episodes.json ADDED
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