Add dataset card and pinned Viewer video config

README.md

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+---
+language:
+- en
+pretty_name: XRSC Bimanual Table Cleanup — Panda + WidowXAI — 10-Episode Inspection Sample
+size_categories:
+- 10K<n<100K
+task_categories:
+- robotics
+configs:
+- config_name: panda
+  data_files:
+  - split: train
+    path: viewer_data/panda/*.parquet
+- config_name: widowxai
+  data_files:
+  - split: train
+    path: viewer_data/widowxai/*.parquet
+- config_name: episodes
+  data_files:
+  - split: train
+    path: viewer_index/metadata.parquet
+- config_name: videos
+  data_files:
+  - split: train
+    path: viewer_videos/train.parquet
+tags:
+- lerobot
+- robot-learning
+- imitation-learning
+- manipulation
+- franka
+- panda
+- widowxai
+- bimanual-manipulation
+- dual-arm
+- table-cleanup
+- multi-view
+- video
+- depth
+- segmentation
+- object-poses
+- 6dof
+- synthetic
+- parquet
+- inspection-sample
+---
+
+# XRSC Bimanual Table Cleanup — Panda + WidowXAI — 10-Episode Inspection Sample
+
+> **10 episodes total: 5 Franka Panda + 5 WidowXAI.** A deliberately small, quality-diverse inspection sample for clearing an optional obstruction and wiping a dirty tabletop with a sponge. Each robot keeps its native LeRobot v2.1 schema in a separate Viewer config. RGB, metric depth, instance segmentation, robot state/action, end-effector trajectories, object poses, and QA annotations are included.
+
+<img src="assets/dataset_preview.gif" alt="XRSC Bimanual Table Cleanup episode and modality preview" width="960">
+
+## ✅ Use it / ❌ Skip it
+
+**Use it for**
+- Inspecting loaders, schemas, camera coverage, depth, segmentation, object poses, annotations, and cross-embodiment differences before using the 150-episode sources.
+- Testing visualization, filtering, QA, and preprocessing pipelines on both Panda and WidowXAI.
+- Reviewing clean and deliberately suboptimal demonstrations. The cleanup sample also includes true task failures.
+
+**Skip it if you need**
+- A from-scratch training corpus. Ten episodes are not sufficient for policy training.
+- A single shared state/action tensor across robots. Panda and WidowXAI retain different joint definitions and vector widths.
+- Real-world data, force/torque, contact, tactile, point-cloud, or audio streams.
+
+## At a glance
+
+| | |
+|---|---|
+| Episodes / frames | **10 / 21,267** (≈11.81 min @ 30 FPS) |
+| Robot split | **5 Panda + 5 WidowXAI** |
+| Selection split | Per robot: **2 successful / 2 suboptimal / 1 cleanup-incomplete failure** |
+| State/action | Panda 18-D; WidowXAI 16-D joint/gripper state; achieved next-frame action, not recorded controller commands |
+| Cameras | 6 RGB views per robot, 1280×960 H.264, no audio |
+| Depth / segmentation | Metric depth and instance segmentation from `top_cam` |
+| Other modalities | End-effector state/action, local/world TCP poses, 6-DoF object state, gripper semantics, phase/subtask QA |
+| Format | Two robot-specific **LeRobot v2.1** subsets + four Hugging Face Viewer configs |
+| Repository footprint | **≈5.70 GB** |
+| License | Not specified in either source repository |
+
+## Load it
+
+The robot frame schemas are intentionally separate. Select the embodiment explicitly:
+
+```python
+from datasets import load_dataset
+
+repo = "VadRobotics/xrsc-cleanup-table-bimanual-inspection-sample"
+panda = load_dataset(repo, name="panda", split="train")
+widowxai = load_dataset(repo, name="widowxai", split="train")
+
+print(panda[0]["observation.state"])
+print(widowxai[0]["observation.state"])
+print(panda[0]["selection_bucket"], panda[0]["source_episode_index"])
+```
+
+Use `name="episodes"` for the combined 10-row provenance and quality index. Use `name="videos"` for the combined RGB browser.
+
+Canonical nested Parquet is stored under each robot root and is best read with PyArrow:
+
+```python
+from huggingface_hub import hf_hub_download
+import pyarrow.parquet as pq
+
+path = hf_hub_download(
+    "VadRobotics/xrsc-cleanup-table-bimanual-inspection-sample",
+    "robots/panda/data/chunk-000/episode_000000.parquet",
+    repo_type="dataset",
+)
+episode = pq.read_table(path)
+print(episode.schema)
+```
+
+## Episode selection
+
+| Robot | Local ep | Source | Source ep | Inspection bucket | Task success | Grade | Score |
+|---|---:|---|---:|---|---|---|---:|
+| panda | 0 | [xrsc-cleanup-table-bimanual-panda-150ep](https://huggingface.co/datasets/VadRobotics/xrsc-cleanup-table-bimanual-panda-150ep/tree/5ecbcce36d5d833449b67d2a39342e287d9be589) | 74 | successful | yes | B | 3.71 |
+| panda | 1 | [xrsc-cleanup-table-bimanual-panda-150ep](https://huggingface.co/datasets/VadRobotics/xrsc-cleanup-table-bimanual-panda-150ep/tree/5ecbcce36d5d833449b67d2a39342e287d9be589) | 89 | successful | yes | B | 3.57 |
+| panda | 2 | [xrsc-cleanup-table-bimanual-panda-150ep](https://huggingface.co/datasets/VadRobotics/xrsc-cleanup-table-bimanual-panda-150ep/tree/5ecbcce36d5d833449b67d2a39342e287d9be589) | 119 | suboptimal | yes | D | 2.14 |
+| panda | 3 | [xrsc-cleanup-table-bimanual-panda-150ep](https://huggingface.co/datasets/VadRobotics/xrsc-cleanup-table-bimanual-panda-150ep/tree/5ecbcce36d5d833449b67d2a39342e287d9be589) | 72 | suboptimal | yes | C | 3.00 |
+| panda | 4 | [xrsc-cleanup-table-bimanual-panda-150ep](https://huggingface.co/datasets/VadRobotics/xrsc-cleanup-table-bimanual-panda-150ep/tree/5ecbcce36d5d833449b67d2a39342e287d9be589) | 131 | failure | no | D | 2.00 |
+| widowxai | 0 | [xrsc-cleanup-table-bimanual-widowxai-150ep](https://huggingface.co/datasets/VadRobotics/xrsc-cleanup-table-bimanual-widowxai-150ep/tree/430e8bbedc8d18e5a7557df3ace7fcf1056ef6ee) | 11 | successful | yes | C | 3.14 |
+| widowxai | 1 | [xrsc-cleanup-table-bimanual-widowxai-150ep](https://huggingface.co/datasets/VadRobotics/xrsc-cleanup-table-bimanual-widowxai-150ep/tree/430e8bbedc8d18e5a7557df3ace7fcf1056ef6ee) | 109 | successful | yes | C | 3.14 |
+| widowxai | 2 | [xrsc-cleanup-table-bimanual-widowxai-150ep](https://huggingface.co/datasets/VadRobotics/xrsc-cleanup-table-bimanual-widowxai-150ep/tree/430e8bbedc8d18e5a7557df3ace7fcf1056ef6ee) | 140 | suboptimal | yes | D | 2.14 |
+| widowxai | 3 | [xrsc-cleanup-table-bimanual-widowxai-150ep](https://huggingface.co/datasets/VadRobotics/xrsc-cleanup-table-bimanual-widowxai-150ep/tree/430e8bbedc8d18e5a7557df3ace7fcf1056ef6ee) | 37 | suboptimal | yes | D | 2.43 |
+| widowxai | 4 | [xrsc-cleanup-table-bimanual-widowxai-150ep](https://huggingface.co/datasets/VadRobotics/xrsc-cleanup-table-bimanual-widowxai-150ep/tree/430e8bbedc8d18e5a7557df3ace7fcf1056ef6ee) | 122 | failure | no | D | 2.29 |
+
+Selection buckets summarize why an episode is present in this inspection sample. Detailed phase annotations, D1–D7 scores, raw measurements, confidence, and source provenance remain available in `annotations.json`, `viewer_index/metadata.parquet`, and `sample_manifest.json`.
+
+## Modalities
+
+- **RGB video:** all source cameras for every selected episode.
+- **Metric depth:** packed float32 NPZ, referenced frame-by-frame from canonical and Viewer Parquet.
+- **Instance segmentation:** lossless FFV1 MKV plus per-row label maps.
+- **Robot state/action:** native, embodiment-specific joint and gripper vectors; no padding or mixed robot tensor.
+- **End-effector:** packed state/action plus local/world TCP poses and linear velocity.
+- **Objects:** per-frame 6-DoF poses, orientation, velocity, and task-specific state.
+- **QA:** task outcome, failure reason, phase ranges, execution quality, task alignment, D1–D7 metrics, composite score, confidence, and grade.
+
+## Source revisions
+
+The sample is reproducibly derived from these immutable private snapshots:
+
+- `VadRobotics/xrsc-cleanup-table-bimanual-panda-150ep@5ecbcce36d5d833449b67d2a39342e287d9be589`
+- `VadRobotics/xrsc-cleanup-table-bimanual-widowxai-150ep@430e8bbedc8d18e5a7557df3ace7fcf1056ef6ee`
+
+Media payloads are byte-identical copies of the selected source files. Episode indices and paths are remapped only inside the five-episode robot subsets. `sample_manifest.json` preserves the complete mapping.
+
+## File layout
+
+```text
+robots/panda/        meta · data · videos · annotations for 5 Panda episodes
+robots/widowxai/     meta · data · videos · annotations for 5 WidowXAI episodes
+viewer_data/         separate flattened panda and widowxai frame configs
+viewer_index/        combined 10-row episode/provenance config
+viewer_videos/       combined RGB Video-feature config
+assets/              dataset_preview.gif showing RGB, depth, and segmentation
+sample_manifest.json · annotations.json
+```
+
+## Notes and limitations
+
+- This is a synthetic inspection sample, not a statistically representative training or evaluation benchmark.
+- Robot schemas are deliberately separate. Do not concatenate state/action vectors without an explicit embodiment adapter.
+- The joint-space and Cartesian actions are derived from achieved next-frame state; no commanded target stream exists.
+- RGB has no audio. Depth and segmentation are available from one task-specific camera.
+- Each robot subset intentionally contains one `cleanup_incomplete` task failure.
+- `successful` and `suboptimal` are inspection-selection buckets; use the original annotations and scores for custom filtering.
+- No standalone license file or explicit license terms were present in the source repositories. Do not assume redistribution rights.
+
+## Citation
+
+If you use this sample, cite this repository plus the exact sample commit and source revisions listed above.

viewer_videos/train.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4f31e1195e96ff66e730c193c7b99579f915edf55523d51d99f875c36ace954b
+size 6282