---
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.