--- license: apache-2.0 language: - en pretty_name: Exylos Bimanual Table Spill Cleanup Sample size_categories: - 10K A human-in-the-loop, multi-view bimanual robot manipulation dataset for tabletop spill cleanup: removing distractor objects and wiping spilled liquid from the surface with a sponge. Delivered in a LeRobot-compatible structure with synchronized video, state/action trajectories, segmentation masks, object pose signals, phase annotations, and success/failure labels. For the Hugging Face Dataset Viewer, `viewer_data/chunk-000/*.parquet` provides a schema-compatible preview mirror of the frame-level data, and `viewer_videos/train.parquet` indexes the RGB video streams. The canonical LeRobot episode parquet files remain in `data/chunk-000/*.parquet`, with external RGB videos and segmentation masks referenced under `videos/`. This 50-episode release does not include depth maps; depth is available only in the related rich-modality sample linked below. --- ## Full Release & Custom Datasets This is a **50-episode public preview** for format inspection, loading tests, and early robot-learning experiments. -> [**Request access / share what you need**](https://forms.gle/9jEK9uVwfhAuUWbEA) (60 seconds) For full-release requirements, reply in the [**full release Discussion**](https://huggingface.co/datasets/ExylosAi/table_spill_cleanup_bimanual/discussions/2). For loading issues or technical feedback, use the **Discussions** tab. --- ## Why this dataset is different Most public manipulation datasets come from one of two sources: real-robot teleoperation farms, which are slow and expensive, or pure simulation, which is cheap but often weak for transfer. This sample comes from a third path: 1. **Human-in-the-loop VR capture.** A human performs the task in an immersive virtual environment using a standard VR headset. Their motion provides task intent, manipulation timing, bimanual coordination, and correction behavior, while the system retargets the demonstration onto a virtual Franka Panda robot embodiment. 2. **Procedurally expanded with added visual domain randomization.** Seed demonstrations are expanded into physics-consistent variations with changing object poses, distractors, mild occlusions, lighting conditions, camera configurations, object materials, and environment appearance. 3. **Packaged for direct inspection and training.** The output is delivered in a LeRobot-compatible structure, with synchronized multi-view video, dual-arm state and action streams, segmentation masks, object pose labels, phase-level annotations, quality scores, and success/failure metadata. The result is human-seeded, scaled, and labeled robot-manipulation data that is closer to what policy training needs, without requiring every trajectory to be collected on a physical robot. This public release is intentionally compact. It is meant as an **inspection sample**: robotics teams can evaluate the format, modalities, visual variation, annotation schema, bimanual trajectory quality, failure semantics, segmentation streams, and object-pose metadata before discussing larger productized skill packs. --- ## Dataset summary | Property | Value | |---|---| | Episodes | 50 | | Total frames | 67,461 | | Total duration | 2,248.70 seconds, about 37.5 minutes | | Task | Remove distractor objects and wipe the spilled liquid from the tabletop with a sponge | | Robot embodiment | Bimanual Franka Emika Panda setup, two 7-DoF arms + parallel grippers | | Camera views | 7 synchronized RGB streams | | RGB video | 30 FPS, H.264, 1280 x 960 | | Segmentation masks | 4 per-frame PNG instance-segmentation streams | | Object pose labels | Per-frame object-state stream with positions, orientation, linear velocity, and dirty-fraction metadata | | Object-state stream | Per-frame `observation.object_poses` list with sponge, cup, and liquid_spill | | Rigid object 6DoF labels | Sponge and cup include 3D position plus quaternion orientation | | Cleanup metric | `liquid_spill.dirty_fraction`, range 0.0 to 1.0 | | Recorded success | Terminal cleanup quality plus failure semantics such as operator aborts; see `next.success` and `annotations.json` | | Robot state | 18-dimensional | | Action vector | 18-dimensional | | Trajectories | Synchronized dual-arm robot state + action streams per frame | | Outcome mix | 27 success episodes, 23 failure episodes | | Failure reasons | 20 cleanup-incomplete failures, 3 operator-abort failures | | Frozen frames | 8,181 total frozen frames across the sample | | Phase-level annotations | approach, grasp, transport, place, retract, clean_surface, clean_swipe_pass, collision, handover, task_attempt | | Episode-level metadata | success/failure outcome, failure reason, duration, frozen-frame count, quality scores, derived metrics | | Visual variation | Object pose, distractors, mild occlusions, lighting, camera configuration, object material, and environment appearance variation | | Format | LeRobot-compatible Parquet + MP4 videos + PNG mask frames | | License | Apache 2.0 | --- ## File layout ```text README.md annotations.json meta/ info.json tasks.jsonl episodes.jsonl episodes_stats.jsonl data/ chunk-000/ episode_000000.parquet ... episode_000049.parquet viewer_data/ chunk-000/ episode_000000.parquet ... episode_000049.parquet viewer_videos/ train.parquet videos/ chunk-000/ observation.images.front_cam/ episode_000000.mp4 ... observation.images.left_cam/ ... observation.images.left_cam_45/ ... observation.images.right_cam/ ... observation.images.top_cam/ ... observation.images.wrist_cam_l/ ... observation.images.wrist_cam_r/ ... observation.masks.top_cam_seg/ episode_000000/ top_cam_seg.FinalImageStencil.0000.png ... observation.masks.left_cam_45_seg/ ... observation.masks.wrist_cam_l_seg/ ... observation.masks.wrist_cam_r_seg/ ... ``` The `viewer_data/` and `viewer_videos/` directories are lightweight Hugging Face Dataset Viewer mirrors. They do not replace the canonical LeRobot files under `data/`, `videos/`, and `meta/`. --- ## Related dataset For a smaller 5-episode inspection sample with additional per-frame depth maps, see [`ExylosAi/table_spill_cleanup_bimanual_rgbd_segmentation_poses`](https://huggingface.co/datasets/ExylosAi/table_spill_cleanup_bimanual_rgbd_segmentation_poses). This larger 50-episode dataset includes RGB video, segmentation masks, object poses, trajectories, and success/failure labels, but it does **not** include depth maps. --- ## What's new in this version This updated sample keeps the same 50 episode IDs but expands the available modalities and refreshes the per-frame metadata: - Adds a seventh RGB camera stream: `observation.images.left_cam_45`. - Adds four per-frame segmentation-mask streams: - `observation.masks.left_cam_45_seg` - `observation.masks.top_cam_seg` - `observation.masks.wrist_cam_l_seg` - `observation.masks.wrist_cam_r_seg` - Adds `observation.object_poses`, a per-frame object-state stream with object IDs, 3D positions, quaternion orientations, finite-difference linear velocities, and remaining dirty-fraction values. - Updates all episode parquet files to include the expanded schema. - Updates `annotations.json` and the LeRobot metadata files under `meta/`. --- ## What is included Each episode bundles synchronized robot, video, mask, pose, and annotation signals: - **Robot state trajectories**: the full 18D bimanual robot state stream over time, covering left and right Panda arm joints plus gripper finger joints. - **Action trajectories**: the 18D control/action signal at each frame for the same bimanual motor ordering. - **Multi-view RGB video**: seven synchronized camera streams: front, left, right, top, left wrist, right wrist, and left 45-degree camera. - **Segmentation masks**: per-frame PNG instance-segmentation masks for top, left 45-degree, left wrist, and right wrist views, plus label-map metadata in the parquet rows. - **Object pose stream**: per-frame object IDs, position, orientation, linear velocity, and remaining dirty-fraction values for tracked task objects. - **Objective cleanup metric**: `dirty_fraction` for the `liquid_spill` object, allowing cleanup quality to be audited from the terminal frame. - **Success/failure semantics tied to cleanup quality and execution status**: terminal `dirty_fraction <= 0.01` means 1% or less of the original spill remains, while recorded success also accounts for failure labels such as operator aborts. - **Per-frame indexing**: timestamp, frame index, episode index, global index, task index, terminal state, and terminal success flag. - **Episode-level metadata**: task identity, success/failure outcome, failure reason, duration, frozen-frame count, quality scores, and derived execution metrics. - **Phase-level annotations**: frame-range segment boundaries for object approach, grasp, transport, placement, retraction, wiping passes, collision events, handovers, and failed task attempts. - **Failure semantics**: selected episodes include cleanup-incomplete, operator-abort, collision, wrong-object, and task-attempt failure signals in annotations and metrics. ### Camera views ```text observation.images.front_cam observation.images.left_cam observation.images.left_cam_45 observation.images.right_cam observation.images.top_cam observation.images.wrist_cam_l observation.images.wrist_cam_r ``` All RGB videos are 1280 x 960 H.264 streams at 30 FPS with no audio. ### Segmentation mask fields ```text observation.masks.left_cam_45_seg observation.masks.top_cam_seg observation.masks.wrist_cam_l_seg observation.masks.wrist_cam_r_seg ``` Segmentation masks are stored as 8-bit grayscale PNG label maps and referenced from parquet rows. The label map is: | Label | Class | |---:|---| | 0 | background | | 1 | left_franka_panda | | 2 | right_franka_panda | | 3 | sponge | | 4 | liquid_spill | | 5 | cup | | 6 | table_surface | Each mask field is represented as a parquet struct with: ```text instance_segmentation: string path to a PNG mask frame label_map: map ``` ### Object pose field ```text observation.object_poses ``` Each row stores a list of object records with: ```text object_id position: float32[3], meters, right-handed world frame orientation: float32[4], quaternion_xyzw, right-handed world frame velocity_linear: float32[3], meters/second, finite difference dirty_fraction: float32, remaining_dirty_fraction in [0.0, 1.0] ``` The stream contains three task objects per frame: ```text sponge cup liquid_spill ``` The `sponge` and `cup` are interactable rigid objects and include 6DoF pose labels: 3D position plus quaternion orientation. The `liquid_spill` is represented as a task-state object: it includes position and the remaining dirty fraction, while its orientation and linear velocity may be null. The objective cleanup metric is: ```text liquid_spill.dirty_fraction ``` This value is a fraction in `[0.0, 1.0]`, where: - `1.0` means 100% of the original dirty area remains. - `0.01` means 1% remains. - `0.0` means the spill is fully cleaned. The terminal cleanup-quality criterion is: ```text terminal_dirty_fraction <= 0.01 ``` The recorded episode outcome is stored in `next.success` on the final frame and in `annotations.json`. It should be treated as authoritative because it also captures failure semantics such as `operator_abort`. ### Core trajectory fields ```text observation.state action timestamp frame_index episode_index index task_index next.done next.success ``` ### Parquet schema Each episode parquet contains 21 columns: ```text timestamp frame_index episode_index index task_index next.done next.success observation.state action observation.object_poses observation.images.front_cam observation.images.left_cam observation.images.top_cam observation.images.right_cam observation.images.wrist_cam_l observation.images.wrist_cam_r observation.images.left_cam_45 observation.masks.top_cam_seg observation.masks.wrist_cam_l_seg observation.masks.wrist_cam_r_seg observation.masks.left_cam_45_seg ``` `next.done` is true on the final frame of each episode. `next.success` is true only on the final frame of successful episodes. ### State and action motor order ```text left_panda_joint1 left_panda_joint2 left_panda_joint3 left_panda_joint4 left_panda_joint5 left_panda_joint6 left_panda_joint7 left_panda_finger_joint1 left_panda_finger_joint2 right_panda_joint1 right_panda_joint2 right_panda_joint3 right_panda_joint4 right_panda_joint5 right_panda_joint6 right_panda_joint7 right_panda_finger_joint1 right_panda_finger_joint2 ``` ### Annotation fields ```text episode_id success task_success failure_reason duration_sec frozen_frames phase_annotations scores derived raw_measurements scorer_id ``` The `phase_annotations` field contains phase names, frame ranges, execution quality, task-alignment labels, hand labels, and optional notes such as grasped object, released object, collision object, or operator-abort context. The `scores`, `derived`, and `raw_measurements` fields provide quality and diagnostic metrics such as path efficiency, grasp precision, placement accuracy, temporal efficiency, motion smoothness, corrective movement score, kinematic headroom, composite score, confidence, path ratio, discontinuity count, and low-frequency motion power. --- ## Loading examples ### Read one episode parquet ```python from pathlib import Path import pyarrow.parquet as pq root = Path("path/to/dataset") episode = root / "data/chunk-000/episode_000000.parquet" table = pq.read_table(episode) df = table.to_pandas() print(df.columns) print(df[["timestamp", "frame_index", "next.done", "next.success"]].tail()) ``` ### Load a referenced segmentation mask ```python from pathlib import Path from PIL import Image import numpy as np import pyarrow.parquet as pq root = Path("path/to/dataset") df = pq.read_table(root / "data/chunk-000/episode_000000.parquet").to_pandas() mask_ref = df["observation.masks.top_cam_seg"].iloc[0] mask = np.array(Image.open(root / mask_ref["instance_segmentation"])) print(mask.shape, mask.dtype, sorted(np.unique(mask).tolist())) print(mask_ref["label_map"]) ``` ### Read the terminal cleanup metric ```python from pathlib import Path import pyarrow.parquet as pq root = Path("path/to/dataset") df = pq.read_table(root / "data/chunk-000/episode_000001.parquet").to_pandas() terminal_objects = df["observation.object_poses"].iloc[-1] spill = next(obj for obj in terminal_objects if obj["object_id"] == "liquid_spill") dirty_fraction = float(spill["dirty_fraction"]) cleanup_quality_pass = dirty_fraction <= 0.01 recorded_success = bool(df["next.success"].iloc[-1]) print("dirty_fraction:", dirty_fraction) print("remaining_percent:", dirty_fraction * 100.0) print("cleanup_quality_pass:", cleanup_quality_pass) print("recorded_success:", recorded_success) ``` --- ## Repository structure ```text README.md annotations.json meta/ info.json tasks.jsonl episodes.jsonl episodes_stats.jsonl data/ chunk-000/ episode_000000.parquet episode_000001.parquet ... videos/ chunk-000/ observation.images.front_cam/ episode_000000.mp4 episode_000001.mp4 ... observation.images.left_cam/ episode_000000.mp4 episode_000001.mp4 ... observation.images.left_cam_45/ episode_000000.mp4 episode_000001.mp4 ... observation.images.right_cam/ ... observation.images.top_cam/ ... observation.images.wrist_cam_l/ ... observation.images.wrist_cam_r/ ... observation.masks.left_cam_45_seg/ episode_000000/ left_cam_45_seg.FinalImageStencil.0000.png left_cam_45_seg.FinalImageStencil.0001.png ... observation.masks.top_cam_seg/ episode_000000/ top_cam_seg.FinalImageStencil.0000.png top_cam_seg.FinalImageStencil.0001.png ... observation.masks.wrist_cam_l_seg/ ... observation.masks.wrist_cam_r_seg/ ... ``` --- ## Intended use This sample is suitable for: - Inspecting the Exylos data format and annotation schema. - Testing LeRobot-compatible training and data-loading pipelines. - Quick imitation-learning experiments on a narrow bimanual spill-cleanup task. - Evaluating synchronized multi-view RGB, dual-arm state/action trajectories, segmentation masks, object poses, and phase-level annotations. - Inspecting visual domain randomization and procedural variation in a compact manipulation sample. - Reviewing success, cleanup-incomplete, operator-abort, collision, wrong-object, and task-attempt failure examples. - Prototyping perception-conditioned policies that use RGB, masks, object-state signals, or combinations of these modalities. For larger production-scale skill packs, including broader object families, configurable embodiments, denser labels, custom evaluation logic, or higher episode volumes, visit [exylos.ai](https://exylos.ai) or contact us directly. --- ## Out-of-scope - This sample does not target a specific real-world deployment cell or production line. - This 50-episode sample does not include depth maps or RGB-D streams. - It does not include a held-out benchmark split tuned for leaderboard-style evaluation. - It is a compact inspection sample rather than a broad benchmark for all spill-cleanup behaviors. - The segmentation masks and object pose labels are provided for inspection, loading, and prototype training; downstream users should validate that the semantics match their own evaluation or policy-training requirements. --- ## Validation notes The local sample was checked for: - 50 parquet files with matching episode IDs from `episode_000000` through `episode_000049`. - 350 RGB MP4 files, 7 camera streams per episode. - 269,844 segmentation PNG files, 4 mask streams per frame. - All parquet references to segmentation mask assets resolve to existing files. - Sampled segmentation masks load as 8-bit grayscale images with labels `0..6`. - `observation.object_poses` contains the expected task objects: `sponge`, `cup`, and `liquid_spill`. - Terminal cleanup quality, `dirty_fraction <= 0.01`, aligns with recorded success except for operator-abort semantics; `next.success` and `annotations.json` remain the authoritative outcome fields. --- ## About Exylos Exylos is an early-stage robotics data company. We capture human manipulation demonstrations in consumer VR and procedurally expand them into physics-consistent, transfer-oriented training episodes with visual domain randomization. Datasets are delivered in LeRobot-compatible structure or adapted to client pipelines. If you are a robotics or applied-ML team and want to discuss a custom skill pack for your embodiment and task, reach out at **contact@exylos.ai** or visit [exylos.ai](https://exylos.ai). --- ## Citation If you use this dataset in research or in a public technical report, please cite it as: ```bibtex @misc{exylos_bimanual_table_spill_cleanup_sample_2026, title = {Exylos Bimanual Table Spill Cleanup Sample: A Multi-View, VR-Captured Sponge-Wiping Dataset}, author = {Exylos}, year = {2026}, howpublished = {\url{https://huggingface.co/datasets/ExylosAi/table_spill_cleanup_bimanual}}, note = {LeRobot-compatible dataset with RGB video, masks, object poses, and phase annotations} } ``` --- ## License Released under the **Apache License 2.0**. This sample is intentionally permissive so robotics and ML teams can inspect, load, test, and commercially evaluate the format without licensing friction. You are free to use this dataset for both research and commercial purposes, subject to the standard Apache 2.0 attribution requirements. --- ## Contact - Website: [exylos.ai](https://exylos.ai) - Email: contact@exylos.ai - LinkedIn: [Exylos on LinkedIn](https://www.linkedin.com/company/exylos-ai/) For questions specific to this dataset, including format, schema, or fields, please open a discussion in the **Community** tab on this repository.