| --- |
| license: apache-2.0 |
| task_categories: |
| - robotics |
| tags: |
| - LeRobot |
| - unitree-g1 |
| - dex3 |
| - pick-and-place |
| - pi0 |
| - vla |
| configs: |
| - config_name: default |
| data_files: data/*/*.parquet |
| --- |
| |
| # pick_and_place-300 — Unitree G1 + Dex3, "pick octopus and place inside brown basket" |
|
|
| LeRobot **v2.1** dataset. Teleoperated bimanual G1 with Dex3 hands; lower body under a GR00T |
| whole-body-control policy, upper body teleoperated. |
|
|
| | | | |
| |---|---| |
| | Episodes | **349** (322 positive demos + 27 negative samples) | |
| | Frames | **161,440** (2.24 h @ 20 fps) | |
| | FPS | **20** | |
| | Cameras | 3 × h264 640×480 yuv420p | |
| | State / action | 43-dim whole body (float64) | |
| | Task string | `pick octopus and place inside brown basket` | |
| | Size | 2.5 GB | |
|
|
| ## Cameras |
|
|
| | Key | Mount | Maps to (pi0.5) | |
| |---|---|---| |
| | `observation.images.ego_view` | head/chest, looks down at the table; sees both hands, object and basket | `base_0_rgb` | |
| | `observation.images.ego_left` | left wrist | `left_wrist_0_rgb` | |
| | `observation.images.ego_right` | right wrist | `right_wrist_0_rgb` | |
|
|
| ## Joint layout — read this before training |
|
|
| `observation.state` and `action` are **43-dim** whole-body vectors: |
|
|
| | Indices | Group | Notes | |
| |---|---|---| |
| | 0–11 | legs | WBC-controlled, not teleoperated | |
| | 12–14 | waist | `enable_waist: false` during collection | |
| | **15–42** | **upper body — the 28 dims to train on** | contiguous, so a slice is enough | |
|
|
| The 28 upper-body dims are ordered **`[L_arm 7, L_hand 7, R_arm 7, R_hand 7]`** — *not* |
| `[arm 14, hand 14]`. Any transform that groups arms against hands must interleave: a delta-action |
| mask is `make_bool_mask(7, -7, 7, -7)`, and `make_bool_mask(14, -14)` would leave the **right** |
| arm (the one performing the task) on absolute actions. |
|
|
| ## Data properties that look like bugs but are not |
|
|
| * **27 episodes are deliberate negative samples**: `51–63, 65–74, 76–79`. The octopus, the |
| basket, or both are absent from the table, so the robot idles and the hand is never commanded. |
| Right-arm motion is 0.269 rad peak-to-peak against 1.649 for real demos. Keep them — they teach |
| the policy not to act on an incomplete scene. |
| * **`left_hand_*` action is bit-identically `0.0`** across all 161,440 frames (all 7 dims). The |
| left hand was never commanded; `0.0` is open/neutral. 7 of the 28 action dims are constant. |
| * **Coupled fingers**: `right_hand_index_1 == right_hand_middle_1` and |
| `right_hand_thumb_1 == right_hand_thumb_2`, bit-identical. Real right-hand DoF is 5, not 7. |
| * **Hand actions overshoot the reachable range** — `right_hand_middle_0` is commanded to 2.98 rad |
| against a reachable 1.56 (`smooth_hand_grasp: true`). **The overshoot is the grip force**; |
| clamping to the observed *state* range would silently weaken every grasp. Clamp to the training |
| *action* envelope instead (`scripts/clamp_spec.json`). |
| * **7 recording sessions**, boundaries at episode indices `0, 80, 131, 174, 184, 311, 338`. |
| They differ in table position, basket (session 4 uses a lighter wicker one), lighting and |
| left-arm rest pose (session 0 notably). Subsample across sessions, not by prefix. |
|
|
| ## Known defects |
|
|
| * **Episodes 172, 173** — full, successful executions with **all three cameras frozen on a single |
| still frame**. 698 frames that teach manipulation succeeding with zero visual change. |
| * **Episodes 146, 147** — `ego_left` frozen for 240 / 402 frames; the other two cameras are fine. |
|
|
| These are retained for completeness. `observation.img_state_delta` is a reliable detector: |
| normal is ~0.033 s, these read 21–595 s. Exclude them with `scripts/make_subset.py` if you want |
| a strictly clean visual set. |
|
|
| ## Missing mode |
|
|
| There is **no post-completion hold**. Positive demos show a median of 5 frames (0.25 s) of |
| trailing stillness; only 1% reach 1 s and none reach 2 s. After release the arm retreats. A |
| policy trained on this alone has no signal to stop after success and will tend to re-attempt the |
| task. |
|
|
| ## Provenance note |
|
|
| This dataset is 7 recording sessions concatenated. The files were renamed contiguously but the |
| in-parquet `episode_index` / `index` columns were originally left at their per-session values, |
| which made 269 of 349 episodes report the wrong `episode_index`. Because LeRobot uses that column |
| to pick the video file *and* to clamp action-chunk boundaries, 77% of samples would have drawn |
| images from the wrong episode — silently, with a healthy-looking loss curve. **This is repaired |
| in the published version** (`scripts/repair_indices.py`, idempotent, `--dry-run` supported). |
| `meta/episodes.jsonl`, `meta/episodes_stats.jsonl` and all 1047 videos were already aligned to |
| filenames and are unchanged. |
|
|
| ## Contents |
|
|
| ``` |
| data/chunk-000/episode_*.parquet 349 files |
| videos/chunk-000/observation.images.{ego_view,ego_left,ego_right}/episode_*.mp4 |
| meta/{info,modality}.json meta/{episodes,episodes_stats,tasks}.jsonl |
| PI05_G1_PICKPLACE_RUNBOOK.md end-to-end pi0.5 SFT runbook -- START HERE |
| PI05_G1_DEX3_SFT_RUNBOOK.md original runbook (superseded; written for a different dataset) |
| RUNBOOK_CORRECTIONS.md what changed between the two, and why |
| g1_dex3_policy.py openpi transforms (43->28 slice, camera mapping) |
| openpi_config_block.py openpi TrainConfig + DataConfig to paste in |
| scripts/repair_indices.py index/counter repair (already applied) |
| scripts/make_subset.py build stratified episode subsets for scaling studies |
| scripts/test_transforms.py verifies the transform pipeline against real rows |
| scripts/check_token_len.py verifies max_token_len is large enough for pi0.5 |
| scripts/verify_download.py checks a downloaded copy is complete and consistent |
| scripts/patch_openpi_config.py applies the policy + train configs to an openpi checkout |
| scripts/check_dataloader.py pulls one sample through the real pipeline before training |
| scripts/prune_checkpoints.py strips train_state from old checkpoints (600 GiB -> 188 GiB) |
| scripts/clamp_spec.json per-joint robot-side clamp limits |
| scripts/upload_to_hf.py publish to the Hub (tags v2.1, guards on index integrity) |
| reference_frames/ frame 0 per recording session, for scene setup |
| ``` |
|
|
| `filter_upper.py`, `add_quantiles.py` and `add_image_stats.py` at the root target a **different** |
| pipeline (LeRobot v3.0 + lerobot-native pi0/pi05 training). They are not used by the runbook here |
| and will not run against a v2.1 layout. Kept for reference. |
|
|
| ## Privacy |
|
|
| Recorded in a shared office. Bystanders are visible and identifiable in the wrist-camera views. |
|
|
