| --- |
| license: mit |
| task_categories: |
| - robotics |
| tags: |
| - manipulation |
| - camera-pose |
| - plucker |
| - progress-estimation |
| - maniskill |
| --- |
| |
| # PickCube-AVM: multi-view PickCube with camera poses and state-derived progress |
|
|
| 2940 clips of ManiSkill `PickCube-v1`, each 32 frames at |
| 256x256, rendered from a known camera pose. Built to test whether a |
| 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`: |
|
|
| | field | meaning | |
| |---|---| |
| | `progress` | per-frame label in [0,1], a pure function of simulator state | |
| | `cam_params` | `intrinsic_cv` (3x3), `extrinsic_cv` (3x4), `eye`, `target`, `fov` | |
| | `states` | per-frame `tcp`, `cube`, `d`, `lift`, `src_frame` | |
| | `cube_frac` / `arm_frac` | per-frame fraction of the image covered by cube / robot | |
| | `kind` | `success`, `failure_missed`, `failure_dropped`, `recovery` | |
|
|
| ```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. |
|
|
| **Geometry is verified, not assumed.** Reprojecting the cube's world position through |
| the stored `extrinsic_cv` and `intrinsic_cv` lands within ~1 px of the rendered cube. |
| 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`. |
|
|
| ## index.json |
|
|
| 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. |
|
|
| ## 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) | |
| |---|---|---|---| |
| | canonical | 1 | 42-42 | 0-0 | |
| | id_random | 4 | 26-45 | 29-56 | |
| | ood_pose | 8 | 7-34 | 86-136 | |
| | ood_fov | 4 | 24-49 | 10-58 | |
| | occlusion | 4 | 12-25 | 141-158 | |
| |
| Trajectories by kind: success 120, recovery 40, failure_missed 20, failure_dropped 20. |
| |
| ## Camera vetting |
| |
| Every battery camera was screened before rendering by replaying trajectories and |
| measuring, from the segmentation, how often the cube is visible. A camera is rejected |
| 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: 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. 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 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 trajectory; filter on |
| `mean_cube_frac` if that matters. |
| |
