| --- |
| 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. |
| |
