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AUTHORS.md

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+# Authorship, contributions, and attribution
+
+TacRich-Manip is created and maintained by **Qingzhu Robotics** and the
+TacRich-Manip dataset contributors.
+
+Institutional contributions represented in this release include:
+
+- robot-system integration and safety validation;
+- task design, teleoperation, UMI collection, and data quality review;
+- multimodal synchronization and LeRobot conversion;
+- Songling PIKA gripper/camera integration;
+- Realman RM75B robot integration; and
+- Tujian Technology (TACHIN) PA-STE electronic-skin integration.
+
+The named author list, affiliations, and CRediT contribution statement will be
+frozen with the public paper release. Until then, cite the machine-readable
+`CITATION.cff` and attribute this private collection-stage dataset to:
+
+> Qingzhu Robotics, TacRich-Manip Dataset Team.
+
+Questions about an exact task release should use the Hugging Face dataset
+repository's discussion channel so the repository and revision remain attached
+to the record.
+
+Copyright 2026 Qingzhu Robotics and dataset contributors.

CITATION.cff

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+cff-version: 1.2.0
+type: dataset
+message: "If you use this dataset, please cite TacRich-Manip and report the immutable Hugging Face commit revision."
+title: "TacRich-Manip LeRobot v3: teleoperation/cyclically_arrange_steel_plate"
+abstract: "Multimodal real-robot trajectories with RGB, depth, tactile sensing, robot state, and absolute actions for gripper-based contact-rich manipulation."
+version: "0.2.0"
+date-released: "2026-08-10"
+authors:
+  - name: "Qingzhu Robotics, TacRich-Manip Dataset Team"
+keywords:
+  - robotics
+  - imitation learning
+  - tactile sensing
+  - contact-rich manipulation
+  - LeRobot
+repository-artifact: "https://huggingface.co/datasets/qingzhu-robotics/TacRich-Manip-LeRobot-teleoperation-cyclically-arrange-steel-plate"
+url: "https://huggingface.co/datasets/qingzhu-robotics/TacRich-Manip-LeRobot-teleoperation-cyclically-arrange-steel-plate"
+license: "Apache-2.0"

LICENSE

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README.md

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+---
+license: apache-2.0
+pretty_name: "TacRich-Manip LeRobot v3 — cyclically_arrange_steel_plate"
+task_categories:
+  - robotics
+size_categories:
+  - 100K<n<1M
+tags:
+  - LeRobot
+  - robot-manipulation
+  - tactile
+  - multimodal
+  - real-robot
+  - contact-rich-manipulation
+---
+
+# TacRich-Manip LeRobot v3 — cyclically_arrange_steel_plate
+
+Multimodal real-robot trajectories for gripper-based contact-rich manipulation,
+published in the standard LeRobot v3 layout. This task repository contains
+`teleoperation/cyclically_arrange_steel_plate` and is private during active collection.
+
+## Dataset summary
+
+| Property | Value |
+| --- | --- |
+| Repository | `qingzhu-robotics/TacRich-Manip-LeRobot-teleoperation-cyclically-arrange-steel-plate` |
+| Collection method | `teleoperation` |
+| Robot | `rm75b-pika-tachin` |
+| Episodes | 80 |
+| Frames | 203,363 |
+| Nominal rate | 40 Hz |
+| Task labels | 17 |
+| LeRobot format | v3.0 |
+
+Robot teleoperation trajectories. `action` is the explicit commanded absolute flange target (`arm_target_*` or `flange_command_*`). Logged gripper-tip poses (`state_gripper_*`/`action_gripper_*` or `arm_tcp_*`/`tcp_target_*`) are used directly after verification; absent poses are derived with T_base_gripper=T_base_flange@T_flange_gripper, translation [0,0,0.2] m and flange-frame Rz(40 deg).
+
+Task labels in `meta/tasks.parquet`:
+
+- `teleoperation/cyclically_arrange_steel_plate:lean_steel_plate_against_cushion/approach_steel_plate_edge`
+- `teleoperation/cyclically_arrange_steel_plate:return_steel_plate_to_rack/stable_grasp_steel_plate`
+- `teleoperation/cyclically_arrange_steel_plate:place_yellow_cushion/place_yellow_cushion_at_target_and_release`
+- `teleoperation/cyclically_arrange_steel_plate:return_steel_plate_to_rack/return_steel_plate_to_rack_and_release`
+- `teleoperation/cyclically_arrange_steel_plate:place_yellow_cushion/grasp_yellow_cushion`
+- `teleoperation/cyclically_arrange_steel_plate:lean_steel_plate_against_cushion/grasp_steel_plate_edge`
+- `teleoperation/cyclically_arrange_steel_plate:return_yellow_cushion_to_original_position/approach_yellow_cushion_for_return`
+- `teleoperation/cyclically_arrange_steel_plate:place_yellow_cushion/approach_yellow_cushion`
+- `teleoperation/cyclically_arrange_steel_plate:return_yellow_cushion_to_original_position/return_yellow_cushion_to_original_position_and_release`
+- `teleoperation/cyclically_arrange_steel_plate:return_yellow_cushion_to_original_position/reset_robot_arm`
+- `teleoperation/cyclically_arrange_steel_plate:return_steel_plate_to_rack/adjust_pose_and_approach_steel_plate`
+- `teleoperation/cyclically_arrange_steel_plate:lean_steel_plate_against_cushion/move_plate_to_cushion_and_release`
+- `teleoperation/cyclically_arrange_steel_plate:return_yellow_cushion_to_original_position/grasp_yellow_cushion_for_return`
+- `teleoperation/cyclically_arrange_steel_plate:place_steel_plate_on_table/grasp_steel_plate`
+- `teleoperation/cyclically_arrange_steel_plate:place_steel_plate_on_table/place_steel_plate_on_table_and_release`
+- `teleoperation/cyclically_arrange_steel_plate:place_steel_plate_on_table/approach_steel_plate`
+- `teleoperation/cyclically_arrange_steel_plate:lean_steel_plate_against_cushion/lift_steel_plate`
+
+## Complete feature inventory
+
+Shapes below are the logical shapes recorded in `meta/info.json`. RGB video is
+stored as MP4 and decoded on demand; other frame fields are stored in Parquet.
+
+| Key | Logical dtype / storage | Shape | Ordered content, unit, and frame |
+| --- | --- | ---: | --- |
+| `observation.images.cam_front` | video / MP4 AV1 | `[480,640,3]` | Front RGB, HWC, uint8-equivalent, current frame |
+| `observation.images.cam_side` | video / MP4 AV1 | `[480,640,3]` | Side RGB, HWC, uint8-equivalent, current frame |
+| `observation.images.cam_fisheye` | video / MP4 AV1 | `[480,640,3]` | Gripper fisheye RGB, HWC, uint8-equivalent, current frame |
+| `observation.depth.cam_front` | image / 16-bit PNG | `[480,640,1]` | Front depth, HW1, uint16 millimetres; stored losslessly |
+| `observation.tactile` | float32 | `[2,32,58]` | `[left_finger, right_finger]`, then sensor row and column; calibrated response, not SI force |
+| `observation.joint_position` | float32 | `[7]` | `[arm_j1,...,arm_j7]`, degrees; zero-filled for UMI only |
+| `observation.ee_pose` | float32 | `[7]` | `[x,y,z,qx,qy,qz,qw]`; metres and unit quaternion in XYZW order |
+| `observation.gripper_distance` | float32 scalar | `[1]` | Current gripper opening, millimetres |
+| `observation.state` | float32 | `[15]` | Joint7 + flange/TCP position3 + quaternion4 + gripper1; exact order below |
+| `observation.state_gripper` | float32 | `[10]` | Gripper-tip/TCP position3 + rotation-6D6 + gripper1; exact order below |
+| `observation.timestamp` | float64 scalar | `[1]` | Aligned source acquisition time, Unix seconds |
+| `observation.source_timestamp_tactile` | float64 scalar | `[1]` | Original tactile sample time, Unix seconds |
+| `observation.source_timestamp_proprio` | float64 scalar | `[1]` | Original robot-state sample time, Unix seconds |
+| `observation.source_timestamp_vision` | float64 scalar | `[1]` | Original front-camera sample time, Unix seconds |
+| `action` | float32 | `[8]` | Absolute target position3 + quaternion4 + target gripper1; exact order below |
+| `action_gripper` | float32 | `[10]` | Gripper-tip/TCP target position3 + rotation-6D6 + target gripper1 |
+| `timestamp` | float32 scalar | `[1]` | LeRobot episode-relative time, `frame_index / 40`, seconds |
+| `frame_index` | int64 scalar | `[1]` | Zero-based frame index inside the episode |
+| `episode_index` | int64 scalar | `[1]` | Zero-based episode identifier in this dataset |
+| `index` | int64 scalar | `[1]` | Zero-based global frame index across all episodes |
+| `task_index` | int64 scalar | `[1]` | Foreign key into `meta/tasks.parquet` |
+
+The four primary vectors are ordered exactly as follows:
+
+```text
+observation.state[15] =
+  [arm_j1, arm_j2, arm_j3, arm_j4, arm_j5, arm_j6, arm_j7,
+   flange_x_m, flange_y_m, flange_z_m,
+   flange_qx, flange_qy, flange_qz, flange_qw,
+   gripper_distance_mm]
+
+action[8] =
+  [target_flange_x_m, target_flange_y_m, target_flange_z_m,
+   target_flange_qx, target_flange_qy, target_flange_qz, target_flange_qw,
+   target_gripper_distance_mm]
+
+observation.state_gripper[10] =
+  [tip_x_m, tip_y_m, tip_z_m,
+   R00, R10, R20, R01, R11, R21,
+   gripper_distance_mm]
+
+action_gripper[10] =
+  [target_tip_x_m, target_tip_y_m, target_tip_z_m,
+   target_R00, target_R10, target_R20,
+   target_R01, target_R11, target_R21,
+   target_gripper_distance_mm]
+```
+
+The rotation-6D representation is the first two **columns** of a 3×3 rotation
+matrix, flattened as `[R[:,0], R[:,1]]`, not the first two rows.
+
+For field-level storage, runtime decoding, timestamp fallback, and metadata
+definitions, see [the complete schema](docs/DATASET_SCHEMA.md).
+
+## Flange and gripper-tip coordinate frames
+
+Teleoperation `observation.state`/`action` use flange poses in the robot-base
+frame. The corresponding `*_gripper` keys use the gripper-tip pose in the same
+robot-base frame. The published fixed transform is
+
+```text
+T_base_gripper = T_base_flange @ T_flange_gripper
+
+T_flange_gripper =
+[[ 0.7660444431, -0.6427876097, 0, 0   ],
+ [ 0.6427876097,  0.7660444431, 0, 0   ],
+ [ 0,             0,            1, 0.2 ],
+ [ 0,             0,            0, 1   ]]
+```
+
+Thus `p_base_gripper = p_base_flange + R_base_flange @ [0,0,0.2]` metres and
+`R_base_gripper = R_base_flange @ Rz(+40°)`. For the reverse direction,
+
+```text
+T_gripper_flange = inverse(T_flange_gripper) =
+[[ 0.7660444431,  0.6427876097, 0,  0   ],
+ [-0.6427876097,  0.7660444431, 0,  0   ],
+ [ 0,             0,            1, -0.2 ],
+ [ 0,             0,            0,  1   ]]
+```
+
+Translation is expressed in the flange frame; yaw is a right-handed local
+rotation about flange +Z. Gripper opening is copied without modification.
+
+UMI is a separate channel: every TCP pose is expressed in the current
+episode's first-valid-TCP frame, `T_local_i = inverse(T_0) @ T_raw_i`, and its
+action is the next observed local TCP pose. Do not mix the two channels without
+respecting this frame convention.
+
+## Loading and visualization
+
+Install a LeRobot version that supports dataset format v3 and the plotting
+dependencies:
+
+```bash
+python -m pip install "lerobot>=0.5" matplotlib numpy
+```
+
+Inspect all keys and their runtime shapes:
+
+```bash
+python examples/load_lerobot_dataset.py --repo-id qingzhu-robotics/TacRich-Manip-LeRobot-teleoperation-cyclically-arrange-steel-plate --episode-index 0
+```
+
+Render front/side/fisheye RGB, lossless uint16 depth, and both tactile maps:
+
+```bash
+python examples/visualize_episode.py \
+  --repo-id qingzhu-robotics/TacRich-Manip-LeRobot-teleoperation-cyclically-arrange-steel-plate \
+  --episode-index 0 \
+  --frame-index 0 \
+  --output episode0_frame0.png
+```
+
+LeRobot loaders normally expose RGB as CHW float tensors. Some torchvision
+versions expose 16-bit PNG depth as a signed `int16` tensor containing the same
+bits; the provided visualizer safely reinterprets those bits as `uint16` before
+plotting. Use the Parquet/Arrow value when exact float64 source timestamps are
+required, because a generic PyTorch scalar conversion can down-cast them.
+
+## File layout and metadata
+
+```text
+README.md, LICENSE, CITATION.cff, AUTHORS.md
+docs/DATASET_SCHEMA.md
+examples/load_lerobot_dataset.py
+examples/visualize_episode.py
+meta/info.json
+meta/stats.json
+meta/tasks.parquet
+meta/episodes/chunk-*/file-*.parquet
+data/chunk-*/file-*.parquet
+videos/<camera-key>/chunk-*/file-*.mp4
+```
+
+`meta/info.json` is the canonical feature/path declaration, `meta/stats.json`
+contains global statistics, `meta/tasks.parquet` maps task text to IDs, and
+`meta/episodes/**` maps every episode to frame and video ranges.
+
+> **Depth-statistics note:** In this release, LeRobot's generic image-statistics path recorded depth as three-channel normalized [0,1] image statistics. Those `meta/stats.json` depth values are not metric millimetre statistics and must not normalize uint16 depth. The stored PNG values and the provided visualizer remain exact.
+
+## Quality, provenance, and limitations
+
+- Conversion checks required fields, shapes, finite pose values, unit
+  quaternions, task/episode counts, and every metadata-referenced shard.
+- Teleoperation actions come only from the migrated absolute `arm_target_*`
+  columns. Raw `action_delta_*` is provenance and is never consumed by this
+  converter. An action is a command at time `t`; servo latency means it is not
+  expected to equal the next measured state exactly.
+- RGB video is lossy AV1; depth and low-dimensional fields are lossless apart
+  from the documented float32 casts.
+- Sensor streams are asynchronous; use the source timestamps to measure age or
+  alignment instead of assuming simultaneous exposure.
+- Tactile values are calibrated sensor responses, not force in newtons unless
+  a separate force calibration is applied.
+- Real-robot trajectories may contain occlusion, lighting changes, contact
+  transients, operator variation, and task failures. Review episodes before
+  safety-critical use.
+
+## Intended use and safety
+
+Intended uses include robot imitation learning, multimodal/tactile
+representation learning, contact-rich manipulation, synchronization research,
+and reproducible format conversion. The dataset does not constitute a safety
+controller or deployment guarantee. Validate workspace limits, action scaling,
+coordinate frames, and emergency-stop behavior on the target robot before any
+real-world execution.
+
+## License, attribution, and citation
+
+This LeRobot dataset is distributed under the
+[Apache License 2.0](LICENSE). Contributor and institutional attribution is in
+[AUTHORS.md](AUTHORS.md). Cite the dataset and record the exact Hugging Face
+commit revision used for experiments; machine-readable citation metadata is in
+[CITATION.cff](CITATION.cff).
+
+```bibtex
+@dataset{tacrich_manip_cyclically_arrange_steel_plate_2026,
+  author  = {{Qingzhu Robotics, TacRich-Manip Dataset Team}},
+  title   = {{TacRich-Manip LeRobot v3: teleoperation/cyclically_arrange_steel_plate}},
+  year    = {2026},
+  version = {0.2.0},
+  url     = {https://huggingface.co/datasets/qingzhu-robotics/TacRich-Manip-LeRobot-teleoperation-cyclically-arrange-steel-plate},
+  note    = {Please also report the immutable Hub commit revision used.}
+}
+```
+
+## Format references
+
+- [LeRobot Dataset v3 specification](https://huggingface.co/docs/lerobot/en/lerobot-dataset-v3)
+- [Hugging Face dataset card guidance](https://huggingface.co/docs/hub/datasets-cards)
+- [LeRobot ALOHA insertion dataset](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human)

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