Publish validated three-holder vector dataset and documentation

README.md

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+---
+language:
+- en
+tags:
+- robotics
+- lerobot
+- arx
+- cable-routing
+- vector-goals
+size_categories:
+- 100K<n<1M
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/chunk-*/*.parquet
+---
+
+# Three-holder cable routing with vector goals
+
+Real-robot demonstrations for a vector-conditioned low-level policy: place three holders and route a cable through each holder. This is the validated LeRobot v3 dataset prepared for the first Pi0.7 four-camera-goal cable policy.
+
+![Six subtask examples: current overhead and wrist observations with a vector-annotated overhead goal](preview.png)
+
+| Property | Value |
+|---|---|
+| Source recordings | 140 |
+| Subtask episodes | 840 |
+| Frames | 335,897 |
+| Sampling rate | 100 Hz |
+| Robot | ARX bimanual |
+| Recorded state / action | 14 / 14 dimensions |
+| Video resolution | 448 × 448 RGB |
+| Split | Train only; no held-out evaluation split |
+
+## Episodes and subtasks
+
+Each source recording becomes six episodes, in order:
+
+1. Place holder 1.
+2. Route the cable through holder 1.
+3. Place holder 2.
+4. Route the cable through holder 2.
+5. Place holder 3.
+6. Route the cable through holder 3.
+
+137 recordings have six original phases. Three recordings have nine original phases; their separate pick phases are merged into the corresponding holder-placement episode. Original phase IDs and labels remain in per-frame metadata. Episode boundaries, rather than video-file boundaries, define training sequences.
+
+There are two task types (placement and routing), distinguished by `task_index` and the stored language instruction. Holder identity and placement vectors are also retained. All 140 recordings belong to the training corpus; this release makes no held-out or novelty claim.
+
+## Observations and vector goals
+
+| Stored video feature | Meaning | Pi0.7 input |
+|---|---|---|
+| `observation.images.video_overhead_raw` | Current clean overhead view | `base_0_rgb` |
+| `observation.images.video_left` | Current left wrist view | `left_wrist_0_rgb` |
+| `observation.images.video_right` | Current right wrist view | `right_wrist_0_rgb` |
+| `observation.images.video_overhead_vector` | Overhead view annotated with the selected holder vector | Source for `goal_0_rgb` |
+
+Native 1280 × 800 images are resized to 448 × 280 and padded with 84 black pixels above and below. The goal overlay uses a yellow center dot (radius 5) and red direction arrow (length 28, width 4, head size 8) at the output resolution. Vector metadata includes native and resized center coordinates and a unit direction in image x/y coordinates.
+
+**The stored fourth video contains the annotated image at each timestamp.** In the training loader, the goal image is sampled uniformly from the current subtask's start through the current frame, inclusive (goal seed 7). This past-goal sampling is a loader operation; simply reading the fourth video at the current timestamp does not reproduce it. The three physical camera inputs use the current frame.
+
+`observation.state`, `observation.velocity`, and `action` preserve the recorded 14-dimensional arrays. Joint ordering and action units require a verified robot-specific deployment binding; they are not inferred here.
+
+## Action chunks and normalization
+
+Stored actions are individual 14-dimensional commands. Training constructs 100-step action chunks and repeats the final action when a chunk reaches a subtask boundary. Chunks must not draw actions from the following subtask. `metadata.valid_future_action_steps` records the remaining valid portion of the chunk.
+
+`norm_stats.json` contains the training normalization statistics, including q01/q99: state statistics use uniform frames, and action statistics account for uniform-frame 100-step chunks with repeated tails. The model consumer pads actions and state to 32 dimensions.
+
+The associated training recipe uses train-time RTC with a uniformly sampled delay of 0–20 steps and checkpoint intervals of 4,000 steps. These settings describe the consuming training pipeline; this repository contains the dataset, not model weights.
+
+## Files and loading
+
+- `data/chunk-000/*.parquet`: frame records, including state, velocity, action, language, vector annotations, source indices and phase labels.
+- `videos/<video_key>/chunk-000/*.mp4`: four synchronized video streams per source recording, with no audio.
+- `meta/info.json`: LeRobot features, counts and file templates.
+- `meta/episodes/chunk-000/file-000.parquet`: subtask episode boundaries and video offsets.
+- `meta/tasks.parquet`: the two task types.
+- `contract.json`: representation, derivation, training contract and SHA-256 artifact manifest.
+- `norm_stats.json`: normalization used for training.
+
+Six subtask episodes share each source recording's data file and videos. A LeRobot v3-compatible reader must use the episode metadata's video file references and `from_timestamp` / `to_timestamp` offsets. Per-episode `frame_index` and `timestamp` restart at zero; these alone are not absolute offsets into a shared video.
+
+Download the native layout:
+
+```python
+from huggingface_hub import snapshot_download
+
+root = snapshot_download(
+    repo_id="ajaysri/pi07_cable_three_vector_v1",
+    repo_type="dataset",
+)
+```
+
+Use a fixed Hub commit via `revision=` when reproducing a run. The Hugging Face table viewer displays frame records; videos are separate files and are not embedded in those rows. The preview above illustrates the visual inputs.
+
+## Integrity
+
+This public upload preserves the validated training artifacts byte-for-byte. The card and preview are additional documentation.
+
+- Dataset content revision: `821d0d27d4e1b5eeb41a65dc72adf3ec72b74dee14d0d31c1f90973a4efead62`
+- Source content revision: `ea6d9dd01f4acfd72369a149c828ec3b96e5e226028221bfec6c9a9e73061cf9`
+- Contract SHA-256: `9b9f047e79a3021bca6e70a26312c3582c69eb540f36b4c6b68fa154a2d2d7ba`
+- Normalization SHA-256: `c7571a4e9b0316bc64d0a13f900f03e31c068ccdbfaac1c104c6f2a39c30dfa1`
+
+The dataset content revision identifies the manifest in `contract.json`; the Hub commit separately identifies this published snapshot.

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