---
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.