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README.md

@@ -17,6 +17,9 @@ tags:
 progress-reward model conditioned on camera geometry generalises to viewpoints it never
 trained on.
 
+Clips are stored as individual `.npz` files in `clips/`, unarchived and unpartitioned --
+pick your own split from the metadata in `index.json`.
+
 ## What is in a clip
 
 Each `.npz` holds `frames` `(T, 256, 256, 3) uint8` and a JSON `meta`:
@@ -29,8 +32,14 @@ Each `.npz` holds `frames` `(T, 256, 256, 3) uint8` and a JSON `meta`:
 | `cube_frac` / `arm_frac` | per-frame fraction of the image covered by cube / robot |
 | `kind` | `success`, `failure_missed`, `failure_dropped`, `recovery` |
 
-**Labels come from physical state, not frame index.** Progress is
-`0.00-0.45` from gripper-to-cube distance, `0.45-0.65` from lift height up to
+```python
+import json, numpy as np
+z = np.load("clips/traj_107__success__canonical.npz", allow_pickle=False)
+frames, meta = z["frames"], json.loads(str(z["meta"]))
+```
+
+**Labels come from physical state, not frame index.** Progress is `0.00-0.45` from
+gripper-to-cube distance, `0.45-0.65` from lift height up to
 0.02m, `0.65-1.00` from cube height toward the goal. Replaying the
 same state later in a clip gives the same label, and a dropped cube's progress falls
 back on its own.
@@ -41,17 +50,28 @@ The intrinsic is computed from the FOV actually set, because ManiSkill's
 `sensor_param["intrinsic_cv"]` keeps reporting the focal length the camera was
 registered with and does not follow `set_fovy`.
 
-## Splits and cameras
+## index.json
 
-| split | trajectories | cameras each | clips |
-|---|---|---|---|
-| train | 140 | 12, resampled per trajectory (~1680 distinct viewpoints) | 1680 |
-| val | 20 | the fixed 21-camera battery | 420 |
-| test | 40 | the fixed 21-camera battery | 840 |
+One record per clip -- `path`, `traj`, `kind`, `cam`, `group`, `mean_cube_frac`,
+`progress_range` -- plus the full camera `battery`. Two fields are worth understanding
+before you split the data:
+
+- **`group`** is the camera's role. `train` means the pose was drawn per trajectory from
+  a continuous distribution (1680 clips, ~140 distinct viewpoints);
+  the other groups are the 21 fixed evaluation poses (1260 clips).
+- **`split`** records which of the two camera regimes a trajectory was rendered under,
+  *not* a partition you have to adopt. A trajectory marked `train` was shot from 12
+  sampled poses and has no battery clips at all; one marked `val`/`test` was shot from
+  the fixed battery and has no sampled ones. So the two regimes are not interchangeable:
+  you cannot ask for a battery view of a `train` trajectory, because it was never
+  rendered. Regroup trajectories freely within a regime; across regimes, check what
+  exists first.
 
-Training viewpoints: azimuth +-60 deg, elevation 20-55 deg, radius 0.50-0.85 m, FOV
-40-60 deg. The evaluation battery is fixed across trajectories so that per-view metrics
-are comparable:
+## Cameras
+
+Sampled (per-trajectory) viewpoints: azimuth +-60 deg, elevation 20-55 deg, radius
+0.50-0.85 m, FOV 40-60 deg. The fixed battery, held constant across trajectories so that
+per-view metrics are comparable:
 
 | group | cameras | elevation (deg) | \|azimuth\| (deg) |
 |---|---|---|---|
@@ -61,7 +81,7 @@ are comparable:
 | ood_fov | 4 | 24-49 | 10-58 |
 | occlusion | 4 | 12-25 | 141-158 |
 
-Trajectory kinds: success 120, recovery 40, failure_missed 20, failure_dropped 20.
+Trajectories by kind: success 120, recovery 40, failure_missed 20, failure_dropped 20.
 
 ## Camera vetting
 
@@ -70,34 +90,22 @@ measuring, from the segmentation, how often the cube is visible. A camera is rej
 and resampled if it is blind for more than 25% of frames on its *worst* probe
 trajectory, or if the cube covers less than 0.10% of the frame there.
 
-This matters: a previous version of this battery had 6 of 21 cameras that never saw the
+This matters: an earlier version of this battery had 6 of 21 cameras that never saw the
 cube at all -- two sat inside the robot base, four looked down from above 65 deg
-elevation where the arm reaches over the cube and occludes it. In this version
-1 of 840 test clips fall below 0.1% cube visibility.
+elevation, where the arm reaches over the cube and occludes it. Here
+1 of 840 battery clips on held-out trajectories fall below
+0.1% cube visibility.
 
 ## Known limitations
 
 - **A frame counter is a strong baseline on `success`.** Progress in a successful demo
   advances monotonically with time, so predicting the mean label per timestep scores
-  Kendall tau +0.97 on success trajectories and +0.86 over the whole labelled test set.
-  Only `recovery` resists it (+0.51). Evaluate there, or report against the frame-counter
-  line explicitly -- a result at or below it is not a result.
+  Kendall tau +0.97 on success trajectories and +0.86 over the whole labelled set. Only
+  `recovery` resists it (+0.51). Evaluate there, or report against the frame-counter line
+  explicitly -- a result at or below it is not a result.
 - **Failures carry no progress target** under the usual masking policy, so 40% of the
   trajectories score only view-consistency metrics.
 - **Elevation extrapolation is not testable on this task.** Above the trained band the
   arm occludes its own workspace, so the battery varies azimuth only.
-- One camera (`oodfov01`, el 49 / az -58) is blind on a single test trajectory; filter
-  on `mean_cube_frac` in `index.json` if that matters.
-
-## Files
-
-`index.json` lists every clip with its split, camera, group, kind, `mean_cube_frac` and
-progress range, plus the full camera battery. Clips are packed as tar shards:
-
-- `train-0000.tar` (1.90 GB)
-- `train-0001.tar` (1.90 GB)
-- `train-0002.tar` (1.90 GB)
-- `train-0003.tar` (0.89 GB)
-- `val-0000.tar` (1.41 GB)
-- `test-0000.tar` (1.90 GB)
-- `test-0001.tar` (0.93 GB)
+- One camera (`oodfov01`, el 49 / az -58) is blind on a single trajectory; filter on
+  `mean_cube_frac` if that matters.

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