Datasets:

qingzhu-robotics
/

TacRich-Manip-LeRobot-teleoperation-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:

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 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:

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

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. 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_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

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