README.md

---
license: apache-2.0
pretty_name: "TacRich-Manip LeRobot v3 — multi-task"
task_categories:
  - robotics
size_categories:
  - 1M<n<10M
tags:
  - LeRobot
  - robot-manipulation
  - tactile
  - multimodal
  - real-robot
  - contact-rich-manipulation
---

# TacRich-Manip LeRobot v3 — multi-task

Multimodal real-robot trajectories for gripper-based contact-rich manipulation,
published in the standard LeRobot v3 layout. Task membership is represented by
each frame's `task_index`, which is a foreign key into `meta/tasks.parquet`.

## Dataset summary

| Property | Value |
| --- | --- |
| Repository | `Tachintech/TacRich-Manip` |
| Collection method | `multi-method` |
| Robot | `rm75b-pika-tachin` |
| Episodes | 823 |
| Frames | 1,315,682 |
| Duration | 9.14 hours at 40 Hz |
| Nominal rate | 40 Hz |
| Task labels | 15 |
| LeRobot format | v3.0 |

This release contains more than one collection method. Consult the task labels and `docs/DATASET_SCHEMA.md`; UMI and teleoperation state/action semantics differ.

## Task taxonomy and collection methods

| Logical task | Collection method | Episodes | Frames | Hours | Published labels | Scope |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| `cyclically_arrange_steel_plate` | `teleoperation` | 79 | 201,043 | 1.40 | 5 | Cyclic steel-plate and cushion arrangement; five major stages and five published task labels. |
| `cyclically_standup_bottle` | `teleoperation` | 181 | 133,682 | 0.93 | 1 | Repeatedly grasp and stand a bottle upright (first collection). |
| `cyclically_standup_bottle_version2` | `teleoperation` | 250 | 219,107 | 1.52 | 1 | Repeated bottle stand-up collection under the version-2 setup. |
| `dining_table_arrangement` | `teleoperation` | 30 | 227,998 | 1.58 | 1 | Long-horizon dining-table arrangement demonstrations. |
| `grasp_objects` | `teleoperation` | 47 | 69,311 | 0.48 | 4 | Return croissant, banana piece, yellow cake roll, and avocado to a plate, published as four object-level task labels. |
| `insert_peg_cylinder` | `teleoperation` | 79 | 109,073 | 0.76 | 1 | Real-machine peg/cylinder insertion demonstrations. |
| `test` | `teleoperation` | 59 | 269,076 | 1.87 | 1 | Unified evaluation/prototyping class containing every small source directory whose name starts with `test-`; all were collected by teleoperation. |
| `set_the_steel_plate` | `umi` | 98 | 86,392 | 0.60 | 1 | UMI-collected steel-plate placement trajectories, kept separate because their episode-local TCP state/action semantics differ from robot teleoperation. |

The two collection methods are deliberately different namespaces. Every
`teleoperation/*` action is an absolute robot command in the robot-base frame.
The `umi/set_the_steel_plate` task uses episode-local TCP coordinates and a
next-observation action target. A training pipeline must not silently treat the
UMI zero-filled joint channels as measured robot joints or mix these coordinate
frames without an explicit adapter.

The small test recordings are intentionally grouped under the single
`teleoperation/test` class in `meta/tasks.parquet`. The following table and
`meta/task_sources.parquet` preserve their source-level provenance without
turning small evaluation recordings into separate training classes.

| Test source | Scope | Episodes | Frames | Hours at 40 Hz |
| --- | --- | ---: | ---: | ---: |
| `test-cyclically_arrange_steel_spoon` | Cyclic steel-spoon arrangement | 3 | 25,025 | 0.174 |
| `test-cyclically_standup_bottle` | Cyclic bottle stand-up | 10 | 71,320 | 0.495 |
| `test-dining-table-arrangement` | Dining-table arrangement | 9 | 69,922 | 0.486 |
| `test-grab_fruit` | Fruit grasping | 5 | 20,129 | 0.140 |
| `test-pour_water` | Water pouring | 3 | 10,330 | 0.072 |
| `test-set_the_tableware-version1` | Tableware placement, setup v1 | 3 | 3,639 | 0.025 |
| `test-set_the_tableware-version2` | Tableware placement, setup v2 | 4 | 7,682 | 0.053 |
| `test-stand_up_objects` | Standing objects upright | 11 | 47,767 | 0.332 |
| `test-wipe_the_blackboard` | Blackboard wiping | 10 | 8,432 | 0.059 |
| `test-write` | Writing | 1 | 4,830 | 0.034 |
| **Test total** | **Unified `teleoperation/test` class** | **59** | **269,076** | **1.869** |

The cyclic steel-plate task publishes only five major task labels: place the steel plate
on the table, place the yellow cushion, lean the plate against the cushion,
return the plate to the rack, and return the cushion/reset the robot. The
object-grasp task similarly publishes only four object-level labels—croissant,
banana piece, yellow cake roll, and avocado. Approach/grasp/transport/release
substage boundaries remain source annotations and are documented provenance;
they are deliberately not separate rows in `meta/tasks.parquet`.

Authoritative task labels stored in `meta/tasks.parquet` are listed below. This
list is rendered directly from the Parquet metadata during publication. In
particular, `grasp_objects` contains four object-level labels, the cyclic
steel-plate task contains five major-stage labels, and all ten small test
sources share one `teleoperation/test` label; no approach/grasp/release
substage is published as a separate task.

- `umi/set_the_steel_plate`
- `teleoperation/grasp_objects:return_yellow_cake_roll_to_plate`
- `teleoperation/grasp_objects:return_banana_piece_to_plate`
- `teleoperation/grasp_objects:return_croissant_to_plate`
- `teleoperation/grasp_objects:return_avocado_to_plate`
- `teleoperation/insert_peg_cylinder`
- `teleoperation/cyclically_standup_bottle`
- `teleoperation/cyclically_standup_bottle_version2`
- `teleoperation/cyclically_arrange_steel_plate:place_yellow_cushion`
- `teleoperation/cyclically_arrange_steel_plate:lean_steel_plate_against_cushion`
- `teleoperation/cyclically_arrange_steel_plate:place_steel_plate_on_table`
- `teleoperation/cyclically_arrange_steel_plate:return_steel_plate_to_rack`
- `teleoperation/cyclically_arrange_steel_plate:return_yellow_cushion_to_original_position`
- `teleoperation/test`
- `teleoperation/dining_table_arrangement`

## 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 Tachintech/TacRich-Manip --episode-index 0
```

Render front/side/fisheye RGB, lossless uint16 depth, and both tactile maps:

```bash
python examples/visualize_episode.py \
  --repo-id Tachintech/TacRich-Manip \
  --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/task_sources.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/task_sources.parquet` preserves physical source-directory provenance
(including the ten inputs grouped into `teleoperation/test`).
`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). It is jointly developed by Tachin Technology,
Qingzhu Robotics, and the TacRich-Manip Dataset Team; Tachin Technology is a
co-development partner and hardware provider. 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_multi-task_2026,
  author  = {{Tachin Technology} and {Qingzhu Robotics} and {TacRich-Manip Dataset Team}},
  title   = {{TacRich-Manip LeRobot v3: multiple tasks}},
  year    = {2026},
  version = {0.2.0},
  url     = {https://huggingface.co/datasets/Tachintech/TacRich-Manip},
  note    = {Dataset version 0.2.0, accessed via Hugging Face. Please report the immutable commit revision used for experiments.}
}
```

## 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)