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_plateteleoperation/grasp_objects:return_yellow_cake_roll_to_plateteleoperation/grasp_objects:return_banana_piece_to_plateteleoperation/grasp_objects:return_croissant_to_plateteleoperation/grasp_objects:return_avocado_to_plateteleoperation/insert_peg_cylinderteleoperation/cyclically_standup_bottleteleoperation/cyclically_standup_bottle_version2teleoperation/cyclically_arrange_steel_plate:place_yellow_cushionteleoperation/cyclically_arrange_steel_plate:lean_steel_plate_against_cushionteleoperation/cyclically_arrange_steel_plate:place_steel_plate_on_tableteleoperation/cyclically_arrange_steel_plate:return_steel_plate_to_rackteleoperation/cyclically_arrange_steel_plate:return_yellow_cushion_to_original_positionteleoperation/testteleoperation/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:
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.
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
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,
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:
python -m pip install "lerobot>=0.5" matplotlib numpy
Inspect all keys and their runtime shapes:
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:
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
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.jsondepth 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. Rawaction_delta_*is provenance and is never consumed by this converter. An action is a command at timet; 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. 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. Cite the dataset and record the exact Hugging Face commit revision used for experiments; machine-readable citation metadata is in CITATION.cff.
@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.}
}