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README.md

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+---
+license: apache-2.0
+task_categories:
+- robotics
+tags:
+- robotics
+- lerobot
+- agilex-piper
+- retargeting
+- co-training
+---
+
+# piper-single-arm-cotrain
+
+Four **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, two from real teleoperation, balanced to the same duration.
+
+| 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.
+
+## Schema
+
+Every archive is a self-contained LeRobot v2.1 dataset:
+
+```
+<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)
+
+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). A third teleop set,
+[`PranayTest/pick-right-2026-08-19-2cam`](https://huggingface.co/datasets/PranayTest/pick-right-2026-08-19-2cam),
+was converted but excluded from this release. `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 session — to match the
+retargeted duration.
+
+All upstream datasets are Apache-2.0.
+
+## Validation
+
+Every episode in all four 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")
+```

mrfood1.tar

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+size 1623326720

mrfood3.tar

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+size 1736755200

piper_ego.tar

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+size 171765760

piper_stera_plate.tar

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+size 75417600