---
tags:
- robotics
- dexterous-manipulation
- bimanual
- pose-estimation
- diffusion-policy
pretty_name: DP-T perceived-pose datasets (bimanual_assembly)
---

# DP-T perceived-pose datasets (bimanual_assembly)

These are training data for perceived-pose policies: the 100 DexJoCo `bimanual_assembly` demos, with `obj_pose` replaced by the poses **our perception** estimated at every step of every demo.

The checkpoints trained on them are in [`b12902136/DP-T-perceived-pose`](https://huggingface.co/b12902136/DP-T-perceived-pose). The code is the DP-T repository, branch `cadfree-pointflow`; how these were made is in [`docs/PERFECT_DEPTH_PIPELINE.md`](https://github.com/unichk/DP-T/blob/cadfree-pointflow/docs/PERFECT_DEPTH_PIPELINE.md).

| Folder | Contents | Size |
|---|---|---|
| `rand_obj_objfut_pointflow_perceived_dpe/bimanual_assembly` | **dpe**: depth tracker on perfect (simulator) depth, every step, new canonical rotations, mesh frame | 31 MB |
| `rand_obj_objfut_pointflow_perceived_rpe/bimanual_assembly` | **rpe**: the same run's RGB tracker poses | 32 MB |
| `every_step_perfect/tracks` | the per-step depth-tracker poses (one json per demo) that dpe was built from | 40 MB |
| `every_step_perfect/tracks_rgb` | the per-step RGB-tracker poses that rpe was built from | 75 MB |
| `every_step_perfect/lite` | per-frame camera poses, robot state and true object poses (no images); the dataset builder's `--frames` | 16 MB |

## Dataset contents (`replay_buffer.zarr`, 54,170 steps, 100 episodes)

| Array | What it is |
|---|---|
| `data/obj_pose` | The policy input: the perceived pose `[tray 7, peg 7]`, position + quaternion (wxyz), in the frame of the shared SAM 3D meshes, at every step (no hold, no interpolation) |
| `data/obj_pose_fut` | The pointflow target: the TRUE motion applied to the perceived pose. Ground truth, used as supervision only. |
| `data/obj_pose_gt` | The true poses, for measurement only. No training config reads it. |
| `data/state`, `data/action`, `data/timestamp`, `meta/episode_ends` | Unchanged from the source dataset `rand_obj_objfut_pointflow` |

`pointflow_stats.npz` holds the pointflow statistics on the perceived cloud. `perceived_pose.json` holds the builder's settings: `--canon --peg-spin hand`, frame `mesh`, every step.

**Rotations.** Rotations are canonicalised:
- the tray's yaw is folded onto one symmetry branch;
- the peg axis points toward the right hand;
- the peg's spin is taken from the right TCP.

The eval perception must use the same rule (`--perceived-canon --perceived-peg-spin hand`).

Perception error against the true pose, as median / p90:

| | Tray | Peg |
|---|---|---|
| dpe | 1.62 / 2.2 mm | 1.10 / 2.3 mm |
| rpe | 1.86 / 6.2 mm | 1.47 / 3.8 mm |

## Use

```bash
# the datasets, into dexjoco's converted_datasets/zarr (where slurm/main reads PV=dpe / rpe)
hf download b12902136/DP-T-perceived-pose-data --repo-type dataset --include 'rand_obj_objfut_pointflow_perceived_*' \
    --local-dir $DEXJOCO_ROOT/converted_datasets/zarr
# (optional) the per-step tracks, to rebuild other representations without re-running perception
hf download b12902136/DP-T-perceived-pose-data --repo-type dataset --include 'every_step_perfect/*' --local-dir $VLA_ROOT/DP-T/outputs/depth

# train (the code repo; also run assets/perceived_pose/install.sh once for the policy's object cloud)
PV=dpe VARIANT=N POSE=perceived SEED=42 sbatch slurm/main/train.sbatch
```

To rebuild a representation, run `scripts/policy/add_perceived_pose_zarr.py` with `--tracks …/every_step_perfect/tracks` (or `tracks_rgb`) and `--frames …/every_step_perfect/lite`, plus the source dataset `rand_obj_objfut_pointflow`. Give it a new tag, since the builder refuses an existing output.

## Caveats

- **Perfect depth.** dpe's depth is the simulator's own, noise-free, so it is an upper bound for a depth sensor.
- **Shared depth model.** The tracker's shared object model was built from these same 100 training demos. It used only depth, with no CAD; the eval scenes were not involved.
- **Assembly only.** These datasets are specific to bimanual_assembly's objects and the DexJoCo demo set.