| --- |
| license: apache-2.0 |
| task_categories: |
| - robotics |
| tags: |
| - robotics |
| - lerobot |
| - agilex-piper |
| - retargeting |
| - co-training |
| --- |
| |
| # piper-single-arm-cotrain |
|
|
| Five **LeRobot v2.1** datasets on an **AgileX Piper** arm, put on one schema so |
| they can be mixed without a rename map: two built by retargeting human |
| demonstrations, three from real teleoperation. Four form a duration-balanced |
| training mixture; the fifth is a held-out validation set. |
|
|
| ### Training mixture |
|
|
| | archive | size | episodes | frames | duration | tasks | provenance | |
| |---|---|---|---|---|---|---| |
| | `piper_ego.tar` | 172 MB | 1,574 | 109,730 | 1 h 31 m | 30 | EgoDex-derived egocentric pick-and-place, retargeted | |
| | `piper_stera_plate.tar` | 75 MB | 226 | 20,385 | 17 m | 127 | stera-10m plate handling, retargeted | |
| | `mrfood1.tar` | 1.62 GB | 294 | 61,709 | 51 m | 1 | real teleop | |
| | `mrfood3.tar` | 1.74 GB | 285 | 68,271 | 57 m | 1 | real teleop | |
|
|
| Retargeted **130,115** frames vs teleop **129,980** — 1 h 48 m per side, 135 |
| frames apart. |
|
|
| ### Validation |
|
|
| | archive | size | episodes | frames | duration | tasks | provenance | |
| |---|---|---|---|---|---|---| |
| | `mrfood2.tar` | 1.85 GB | 347 | 67,991 | 57 m | 1 | real teleop, held out | |
|
|
| `mrfood2` is a **separate teleop recording session** from the two in the |
| training mixture (2026-08-19, against 08-14/15 and 08-11), carrying the same |
| schema and the same transforms — but **untrimmed**, since a validation set |
| should not be truncated to a training budget. |
|
|
| Read what it measures narrowly: it is the same task (`pick_and_lift_right`) on |
| the same rig, so it scores **cross-session generalisation within the teleop |
| domain**. Its single task is exactly the one the teleop training half already |
| covers. It says nothing about the retargeted domain, which contributes 157 of |
| the mixture's 158 distinct task strings (the two halves share no task) and all |
| of its synthesized imagery. A model that |
| scores well here has not been shown to transfer to the retargeted distribution, |
| or to any unseen task. |
|
|
| ## Schema |
|
|
| Every archive — training and validation alike — is a self-contained LeRobot |
| v2.1 dataset with an identical schema: |
|
|
| ``` |
| <dataset>/data/chunk-000/episode_NNNNNN.parquet |
| <dataset>/videos/chunk-000/observation.images.top/episode_NNNNNN.mp4 |
| <dataset>/videos/chunk-000/observation.images.right-arm/episode_NNNNNN.mp4 |
| <dataset>/meta/{info,episodes,tasks,episodes_stats}.jsonl + info.json |
| ``` |
|
|
| | | | |
| |---|---| |
| | `codebase_version` | `v2.1` | |
| | `robot_type` | `agilex_piper` (single arm) | |
| | rate | 20 Hz / 20 fps | |
| | `observation.state`, `action` | `float32[7]` = `[j1…j6 in degrees, gripper ∈ {0,1}]` | |
| | cameras | `observation.images.top`, `observation.images.right-arm` | |
|
|
| Gripper is `0` = closed, `1` = open. Joint angles are **degrees**, within the |
| Piper limits (±150, 0–180, −155–0, ±105, ±70, ±180). |
|
|
| `meta/source_episodes.jsonl` maps each episode back to its index in the |
| pre-trim dataset; `meta/source_clips.jsonl` (retargeted) and |
| `meta/episode_quality.jsonl` (teleop) are carried through where the source had |
| them. |
|
|
| ## How these were unified |
|
|
| The sources did not agree, in ways that raise no error at load time: |
|
|
| - **Left arm dropped.** It was a constant park pose in the retargeted sets and |
| exactly zero in the teleop sets — no signal either way. This also moves the |
| gripper to index 6 everywhere; it had been at index 6 in the retargeted sets |
| and index **12** in teleop. |
| - **Radians → degrees** on the retargeted sets. Teleop was already degrees. |
| - **Gripper binarized.** Teleop was already effectively binary (one transition |
| per clip), so a plain 0.4 threshold applies. The retargeted gripper is a |
| continuous hand-aperture signal that never reaches either stop; a plain |
| threshold on it yields a median of 3–4 transitions per clip — phantom |
| grasp/release events inside a single pick-and-place. It instead gets per-clip |
| min/max normalisation and a Schmitt trigger (0.40 ±0.15, 8-frame dwell), |
| giving a median of 2 transitions (grasp + release). |
| - **20 Hz.** The retargeted sets were resampled from 30 Hz by linear |
| interpolation onto a uniform 50 ms grid (30→20 is a 2/3 ratio, so decimation |
| would leave alternating 33/67 ms gaps), with the action recomputed afterwards. |
|
|
| Gripper open-fraction after binarization: 0.64 / 0.49 (retargeted) against |
| 0.56 / 0.51 (teleop). |
|
|
| ## Known limits |
|
|
| **The retargeted views are synthetic, and share one camera.** `top` and |
| `right-arm` are both crops of a *single* egocentric frame — `top` is the square |
| centre crop, `right-arm` a zoom window tracking a MediaPipe-detected grasp |
| point. They carry no parallax, unlike the teleop sets' two physical cameras. |
| Video is 224×224 upright for the retargeted sets and 480×640 portrait |
| (rotated ~90°) for teleop. |
|
|
| **Action means slightly different things.** The retargeted action is exactly |
| `state[t+1]` — a perfect next-frame target, because it is constructed that way. |
| The teleop action is a separately recorded controller command that leads the |
| state by one frame with a real tracking residual (~0.08°). Both are absolute |
| next-step position targets. |
|
|
| **Joint envelopes only partly overlap.** The retargeted trajectories reach every |
| Piper joint limit; the teleop trajectories occupy a narrow interior band. About |
| 73% of the retargeted saturation is wrist-driven (joint4/joint5; joint5's range |
| is only ±1.22 rad, the tightest on the arm). |
|
|
| **`piper_stera_plate` is the weakest set.** Its retargeting FK error is |
| substantially higher than `piper_ego`'s, and 10% of its clips still show more |
| than four gripper transitions after debouncing. |
|
|
| **Grasp events are inferred, not labelled.** The retargeted gripper signal comes |
| from hand pose, not from a recorded gripper command. |
|
|
| ## Provenance and attribution |
|
|
| - `piper_ego`, `piper_stera_plate` — retargeted with the Fidelity Dynamics |
| dt-pipeline (stage 6) from |
| [`angkul07/piper-retargeted`](https://huggingface.co/datasets/angkul07/piper-retargeted), |
| itself derived from EgoDex-style egocentric recordings and stera-10m. |
| - `mrfood1` — from [`PranayTest/pick-right-2026-08-14to15-2cam`](https://huggingface.co/datasets/PranayTest/pick-right-2026-08-14to15-2cam) |
| - `mrfood3` — from [`PranayTest/pick-right-2026-08-11-2cam-prxxx`](https://huggingface.co/datasets/PranayTest/pick-right-2026-08-11-2cam-prxxx) |
| - `mrfood2` — from [`PranayTest/pick-right-2026-08-19-2cam`](https://huggingface.co/datasets/PranayTest/pick-right-2026-08-19-2cam) |
|
|
| The teleop sources are LeRobot v3.0 and were converted to v2.1 here (v3.0 packs |
| many episodes per parquet and concatenates episodes into shared video files; |
| splitting those requires a re-encode, since the cut points are not |
| keyframe-aligned). `mrfood1` and `mrfood3` were then trimmed by dropping whole |
| episodes in a golden-ratio order — so the cut spreads evenly across each run |
| rather than concentrating on one stretch of a session — to match the retargeted |
| duration. `mrfood2` is untrimmed. |
|
|
| All upstream datasets are Apache-2.0. |
|
|
| ## Validation |
|
|
| Every episode in all five archives passes: parquet row count against declared |
| length, contiguous `frame_index`, timestamps against the declared rate, |
| globally continuous `index`, and a frame count for each declared camera key. |
| Zero errors. |
|
|
| ## Usage |
|
|
| ```bash |
| tar xf piper_ego.tar |
| ``` |
|
|
| ```python |
| from lerobot.common.datasets.lerobot_dataset import LeRobotDataset |
| ds = LeRobotDataset("piper_ego", root="piper_ego") |
| ``` |
|
|
