--- language: - en pretty_name: Cross-Embodiment Bimanual Table Cleanup — Rich-Modality 10-Episode Inspection Sample size_categories: - 10K **10 full-modality cross-embodiment bimanual table-cleanup episodes:** 5 Franka Panda + 5 WidowXAI, 21,267 frames, 6 RGB views per robot, task-camera depth and segmentation, native robot state/action, end-effector trajectories, 6-DoF object poses, and QA annotations. Cross-embodiment bimanual table cleanup rich-modality preview ## ✅ 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 | **Additional camera modalities:** Any RGB view can be supplied with additional synthetic modalities, including metric depth and instance segmentation, on request. These modalities are not included in this release unless explicitly listed above. ## Load it The robot frame schemas are intentionally separate. Select the embodiment explicitly: ```python from datasets import load_dataset repo = "ExylosAi/bimanual-table-cleanup-cross-embodiment-rich-modality-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( "ExylosAi/bimanual-table-cleanup-cross-embodiment-rich-modality-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.