diff --git a/AUTHORS.md b/AUTHORS.md new file mode 100644 index 0000000000000000000000000000000000000000..0c38a2e897873ba9b4be831ab050d68efd0bba58 --- /dev/null +++ b/AUTHORS.md @@ -0,0 +1,25 @@ +# 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. diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000000000000000000000000000000000000..03549948134b2d131c469735ca8e3c5e7d3e0dce --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,18 @@ +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" diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d645695673349e3947e8e5ae42332d0ac3164cd7 --- /dev/null +++ b/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..df136784cf1d3f72abbc6914952b0d45c12cdfb2 --- /dev/null +++ b/README.md @@ -0,0 +1,256 @@ +--- +license: apache-2.0 +pretty_name: "TacRich-Manip LeRobot v3 — cyclically_arrange_steel_plate" +task_categories: + - robotics +size_categories: + - 100K=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//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) diff --git a/data/chunk-000/file-000.parquet 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100644 index 0000000000000000000000000000000000000000..cfadf4f39cc4dae3872fad7e2c5140e86147a5e3 --- /dev/null +++ b/docs/DATASET_SCHEMA.md @@ -0,0 +1,217 @@ +# TacRich-Manip LeRobot v3 — complete schema + +This document is the normative field reference for TacRich-Manip LeRobot task +repositories. `meta/info.json` remains the machine-readable source of shapes, +dtypes, FPS, and path templates. The dataset card summarizes the same fields; +this file expands every key, coordinate frame, unit, order, and storage detail. + +## Shape and runtime conventions + +- Logical image shapes in `meta/info.json` use `[height,width,channel]`. +- LeRobot commonly returns decoded RGB as PyTorch `[channel,height,width]` + float32 in `[0,1]`; this runtime layout is not a change to the stored schema. +- Scalar features are declared with shape `[1]` but are serialized as scalar + Parquet values and may be returned as zero-dimensional tensors. +- `float32` vectors have expected quantization at approximately 1e-7 relative + precision. Source timestamps are stored as Parquet `float64`. +- Quaternion order is always `[qx,qy,qz,qw]`; positions are `[x,y,z]`. + +## Complete per-frame feature dictionary + +### Visual and tactile observations + +| Key | `info.json` dtype | Logical shape | Storage and value definition | +| --- | --- | ---: | --- | +| `observation.images.cam_front` | `video` | `[480,640,3]` | Front RGB at time `t`; MP4 AV1, 40 Hz nominal, HWC RGB/uint8-equivalent before loader conversion | +| `observation.images.cam_side` | `video` | `[480,640,3]` | Side RGB at time `t`; same format | +| `observation.images.cam_fisheye` | `video` | `[480,640,3]` | Gripper fisheye RGB at time `t`; same format | +| `observation.depth.cam_front` | `image` | `[480,640,1]` | Front depth at time `t`; lossless 16-bit PNG, uint16, millimetres, stored in the data Parquet image struct | +| `observation.tactile` | `float32` | `[2,32,58]` | PA-STE response indexed `[finger,row,column]`; finger 0 = left/NPZ key `0`, finger 1 = right/NPZ key `1` | + +RGB video keys are not columns in `data/**/*.parquet`; LeRobot resolves them +through the video ranges in `meta/episodes/**/*.parquet`. This is normal v3 +layout, not missing conversion. Depth is deliberately a Parquet image field so +the 16-bit PNG bytes remain lossless. Tactile response is dimensionless after +sensor calibration and must not be interpreted as newtons without a separate +force calibration. + +### Robot observations and actions + +| Key | Storage dtype | Shape | Definition | +| --- | --- | ---: | --- | +| `observation.joint_position` | float32 | `[7]` | Current robot joints `[arm_j1,...,arm_j7]`, degrees; all zeros for UMI when joints are unavailable | +| `observation.ee_pose` | float32 | `[7]` | Current flange/TCP pose `[x,y,z,qx,qy,qz,qw]`; position metres, orientation unit quaternion | +| `observation.gripper_distance` | float32 scalar | `[1]` | Current opening distance, millimetres | +| `observation.state` | float32 | `[15]` | Current joint + flange/TCP + gripper state; exact index table below | +| `observation.state_gripper` | float32 | `[10]` | Current gripper-tip/TCP pose in rotation-6D form plus opening; exact index table below | +| `action` | float32 | `[8]` | Absolute flange/TCP target plus gripper target; exact index table below | +| `action_gripper` | float32 | `[10]` | Absolute gripper-tip/TCP target in rotation-6D form plus target opening | + +#### `observation.state` indices + +| Index | Name | Unit | Frame / meaning | +| ---: | --- | --- | --- | +| 0–6 | `arm_j1` … `arm_j7` | degree | Current robot joints; UMI zero-fill only | +| 7 | `flange_x_m` | m | Current flange/TCP X | +| 8 | `flange_y_m` | m | Current flange/TCP Y | +| 9 | `flange_z_m` | m | Current flange/TCP Z | +| 10 | `flange_qx` | 1 | Quaternion X, XYZW order | +| 11 | `flange_qy` | 1 | Quaternion Y | +| 12 | `flange_qz` | 1 | Quaternion Z | +| 13 | `flange_qw` | 1 | Quaternion W | +| 14 | `gripper_distance_mm` | mm | Current opening distance | + +#### `action` indices + +| Index | Name | Unit | Frame / meaning | +| ---: | --- | --- | --- | +| 0 | `target_flange_x_m` | m | Absolute target X | +| 1 | `target_flange_y_m` | m | Absolute target Y | +| 2 | `target_flange_z_m` | m | Absolute target Z | +| 3 | `target_flange_qx` | 1 | Target quaternion X, XYZW order | +| 4 | `target_flange_qy` | 1 | Target quaternion Y | +| 5 | `target_flange_qz` | 1 | Target quaternion Z | +| 6 | `target_flange_qw` | 1 | Target quaternion W | +| 7 | `target_gripper_distance_mm` | mm | Target opening distance | + +#### `observation.state_gripper` and `action_gripper` indices + +| Index | State name | Action name | Unit | Definition | +| ---: | --- | --- | --- | --- | +| 0 | `tip_x_m` | `target_tip_x_m` | m | Gripper-tip/TCP X | +| 1 | `tip_y_m` | `target_tip_y_m` | m | Gripper-tip/TCP Y | +| 2 | `tip_z_m` | `target_tip_z_m` | m | Gripper-tip/TCP Z | +| 3 | `R00` | `target_R00` | 1 | First rotation-matrix column, row 0 | +| 4 | `R10` | `target_R10` | 1 | First column, row 1 | +| 5 | `R20` | `target_R20` | 1 | First column, row 2 | +| 6 | `R01` | `target_R01` | 1 | Second column, row 0 | +| 7 | `R11` | `target_R11` | 1 | Second column, row 1 | +| 8 | `R21` | `target_R21` | 1 | Second column, row 2 | +| 9 | `gripper_distance_mm` | `target_gripper_distance_mm` | mm | Opening distance | + +Rotation-6D is therefore `concat(R[:,0], R[:,1]) = +[R00,R10,R20,R01,R11,R21]`. A consumer may reconstruct an orthonormal matrix +by Gram–Schmidt normalization of the two stored columns. + +### Time, episode, and indexing keys + +| Key | Parquet dtype | Shape | Definition | +| --- | --- | ---: | --- | +| `observation.timestamp` | float64 | `[1]` | Aligned raw CSV timestamp, Unix seconds | +| `observation.source_timestamp_tactile` | float64 | `[1]` | Tactile source timestamp; explicit CSV value, otherwise filename timestamp, otherwise aligned timestamp | +| `observation.source_timestamp_proprio` | float64 | `[1]` | Proprioception source timestamp; explicit CSV value, otherwise aligned timestamp | +| `observation.source_timestamp_vision` | float64 | `[1]` | Front RGB source timestamp; explicit CSV value, otherwise filename timestamp, otherwise aligned timestamp | +| `timestamp` | float32 | `[1]` | LeRobot relative time in seconds: `frame_index / fps` | +| `frame_index` | int64 | `[1]` | Zero-based frame number within one episode | +| `episode_index` | int64 | `[1]` | Zero-based episode ID across the dataset | +| `index` | int64 | `[1]` | Zero-based global row ID across all episodes | +| `task_index` | int64 | `[1]` | Index into `meta/tasks.parquet` | + +Do not use `timestamp` as wall-clock time. Conversely, do not subtract large +Unix timestamps after a loader has cast them to float32: epoch-scale float32 +has coarse resolution. Read the Parquet float64 values or subtract in float64 +first when precise sensor synchronization is required. + +## Coordinate frames and fixed flange-to-tip transform + +Let `B` be the robot base, `F` the flange frame, and `G` the published +gripper-tip/TCP frame. Homogeneous transforms use the convention `T_A_B` = pose +of frame B expressed in frame A. `T_F_G` is the same transform elsewhere named +`T_flange_gripper`. Teleoperation uses + +```text +T_B_G = T_B_F @ T_F_G +``` + +with the exact configured values + +```text +translation_F_G_m = [0.0, 0.0, 0.2] +yaw_F_G_deg = +40.0 +cos(40 deg) = 0.7660444431 +sin(40 deg) = 0.6427876097 + +T_F_G = +[[ 0.7660444431, -0.6427876097, 0.0, 0.0 ], + [ 0.6427876097, 0.7660444431, 0.0, 0.0 ], + [ 0.0, 0.0, 1.0, 0.2 ], + [ 0.0, 0.0, 0.0, 1.0 ]] + +T_G_F = inverse(T_F_G) = +[[ 0.7660444431, 0.6427876097, 0.0, 0.0 ], + [-0.6427876097, 0.7660444431, 0.0, 0.0 ], + [ 0.0, 0.0, 1.0, -0.2 ], + [ 0.0, 0.0, 0.0, 1.0 ]] +``` + +Equivalently: + +```text +p_B_G = p_B_F + R_B_F @ [0,0,0.2] +R_B_G = R_B_F @ Rz(+40 degrees) +``` + +The translation is in the flange frame, so it must be rotated by the current +flange orientation. `observation.state` contains joints plus `T_B_F`; +`observation.state_gripper` omits joints and contains `T_B_G` as position + +rotation-6D. The same distinction applies to `action` and `action_gripper`. + +## Collection-method action semantics + +### Teleoperation + +- State poses are absolute in the robot-base frame. +- `action[t]` is the migrated, applied absolute flange command from raw + `arm_target_*` at aligned row `t`; only those columns are read. +- Raw `action_delta_*` is retained in the raw archive as provenance but is not + read by either LeRobot or AVAloha conversion. +- `action_gripper[t]` is obtained from the same target with `T_B_G = T_B_F @ + T_F_G`; no independent zero or second clamp is applied. +- Commanded action and `state[t+1]` should follow the same trajectory, but they + are not mathematical equality because command look-ahead, robot dynamics, + controller filtering, and sensor latency are real. + +### UMI + +Let `T_0` be the first valid TCP pose of the episode: + +```text +T_local_i = inverse(T_0) @ T_raw_i +p_local_i = R_0^T @ (p_i - p_0) +R_local_i = R_0^T @ R_i +``` + +The first valid state is zero translation with identity rotation. The action at +`t` is the next observed local TCP pose; the final action repeats the final +state. Joint fields are zero-filled because the UMI source has no robot joints. + +## LeRobot v3 files and metadata keys + +| File or directory | Complete purpose | +| --- | --- | +| `meta/info.json` | `codebase_version`, `robot_type`, totals, FPS, splits, chunk/file sizes, path templates, and all feature dtype/shape/name declarations | +| `meta/stats.json` | Global min/max/mean/std/count and quantiles used for normalization/audit | +| `meta/tasks.parquet` | Task strings and their integer `task_index` values | +| `meta/episodes/chunk-*/file-*.parquet` | Per-episode length/tasks, global data bounds, data shard IDs, video shard/time ranges, and episode statistics | +| `data/chunk-*/file-*.parquet` | All non-video per-frame keys listed above | +| `videos//chunk-*/file-*.mp4` | AV1 RGB frames; multiple episodes may share a v3 video shard | + +## Exact-depth loading note + +The PNG bytes are uint16 and were verified lossless against source depth. With +some torchvision versions, `LeRobotDataset` returns a signed `torch.int16` +depth tensor because PIL mode `I;16` is passed through `ToTensor`. The bit +pattern is still exact; recover it before numeric use: + +```python +depth = sample["observation.depth.cam_front"].cpu().numpy().squeeze() +if depth.dtype == np.int16: + depth = depth.view(np.uint16) +else: + depth = depth.astype(np.uint16, copy=False) +``` + +The provided `examples/visualize_episode.py` performs this conversion and does +not silently rescale depth to 8-bit. + +**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. diff --git a/examples/load_lerobot_dataset.py b/examples/load_lerobot_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..32c0ce3c57904bcfca89128b09816d6aa39a6fdb --- /dev/null +++ b/examples/load_lerobot_dataset.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python3 +"""Load one TacRich-Manip episode and print every key, shape, dtype, and value order.""" + +from __future__ import annotations + +import argparse +from pathlib import Path + + +def lerobot_dataset_class(): + try: + from lerobot.datasets.lerobot_dataset import LeRobotDataset + except ModuleNotFoundError: + from lerobot.common.datasets.lerobot_dataset import LeRobotDataset + return LeRobotDataset + + +def describe(value: object) -> str: + shape = tuple(getattr(value, "shape", ())) + dtype = getattr(value, "dtype", type(value).__name__) + return f"shape={shape}, dtype={dtype}" + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--repo-id", default="qingzhu-robotics/TacRich-Manip-LeRobot-teleoperation-cyclically-arrange-steel-plate") + parser.add_argument("--revision", default="main") + parser.add_argument("--root", type=Path, help="Optional existing local dataset root") + parser.add_argument("--episode-index", type=int, default=0) + parser.add_argument("--frame-index", type=int, default=0, + help="Zero-based index inside the selected episode") + parser.add_argument("--video-backend", default="pyav") + args = parser.parse_args() + + dataset = lerobot_dataset_class()( + repo_id=args.repo_id, + root=args.root, + revision=args.revision, + episodes=[args.episode_index], + video_backend=args.video_backend, + ) + if not 0 <= args.frame_index < len(dataset): + raise IndexError(f"frame {args.frame_index} outside [0, {len(dataset) - 1}]") + sample = dataset[args.frame_index] + + print(dataset) + print(f"selected episode={args.episode_index}, local frame={args.frame_index}") + print("runtime sample keys:") + for key in sorted(sample): + print(f" {key:48s} {describe(sample[key])}") + + print("\nordered vectors:") + for key in ( + "observation.state", + "action", + "observation.state_gripper", + "action_gripper", + ): + print(f" {key}: {sample[key]}") + + print("\nCanonical orders are documented in docs/DATASET_SCHEMA.md.") + print("For exact float64 source times, read the Parquet columns with PyArrow.") + + +if __name__ == "__main__": + main() diff --git a/examples/visualize_episode.py b/examples/visualize_episode.py new file mode 100644 index 0000000000000000000000000000000000000000..b19b689d832a2cbf3346ed2bc22d45730c0eaf12 --- /dev/null +++ b/examples/visualize_episode.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python3 +"""Render the three RGB views, exact uint16 depth, and both tactile surfaces.""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np + + +RGB_KEYS = ( + ("observation.images.cam_front", "Front RGB"), + ("observation.images.cam_side", "Side RGB"), + ("observation.images.cam_fisheye", "Fisheye RGB"), +) + + +def lerobot_dataset_class(): + try: + from lerobot.datasets.lerobot_dataset import LeRobotDataset + except ModuleNotFoundError: + from lerobot.common.datasets.lerobot_dataset import LeRobotDataset + return LeRobotDataset + + +def to_numpy(value: object) -> np.ndarray: + if hasattr(value, "detach"): + value = value.detach().cpu().numpy() + return np.asarray(value) + + +def rgb_hwc(value: object) -> np.ndarray: + image = to_numpy(value) + if image.ndim != 3: + raise ValueError(f"expected RGB rank 3, got {image.shape}") + if image.shape[0] in (1, 3, 4) and image.shape[-1] not in (1, 3, 4): + image = np.moveaxis(image, 0, -1) + if image.shape[-1] == 4: + image = image[..., :3] + if np.issubdtype(image.dtype, np.integer): + image = image.astype(np.float32) / 255.0 + return np.clip(image, 0.0, 1.0) + + +def exact_depth_uint16(value: object) -> np.ndarray: + depth = np.ascontiguousarray(np.squeeze(to_numpy(value))) + if depth.ndim != 2: + raise ValueError(f"expected depth HW/HW1/1HW, got {depth.shape}") + if depth.dtype == np.int16: + return depth.view(np.uint16) + if depth.dtype == np.uint16: + return depth + if np.issubdtype(depth.dtype, np.floating) and float(np.nanmax(depth)) <= 1.0: + raise TypeError( + "depth was normalized to [0,1]; exact millimetres are unavailable from " + "this loader version. Read the PNG bytes from data/*.parquet instead." + ) + return depth.astype(np.uint16, copy=False) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--repo-id", default="qingzhu-robotics/TacRich-Manip-LeRobot-teleoperation-cyclically-arrange-steel-plate") + parser.add_argument("--revision", default="main") + parser.add_argument("--root", type=Path, help="Optional existing local dataset root") + parser.add_argument("--episode-index", type=int, default=0) + parser.add_argument("--frame-index", type=int, + help="Episode-local frame; default is the midpoint") + parser.add_argument("--video-backend", default="pyav") + parser.add_argument("--depth-max-mm", type=float, default=2000.0) + parser.add_argument("--tactile-vmax", type=float, + help="Shared tactile upper limit; default uses current-frame max") + parser.add_argument("--output", type=Path, default=Path("episode_preview.png")) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + dataset = lerobot_dataset_class()( + repo_id=args.repo_id, + root=args.root, + revision=args.revision, + episodes=[args.episode_index], + video_backend=args.video_backend, + ) + frame_index = len(dataset) // 2 if args.frame_index is None else args.frame_index + if not 0 <= frame_index < len(dataset): + raise IndexError(f"frame {frame_index} outside [0, {len(dataset) - 1}]") + if args.depth_max_mm <= 0: + raise ValueError("--depth-max-mm must be positive") + sample = dataset[frame_index] + + depth = exact_depth_uint16(sample["observation.depth.cam_front"]) + tactile = to_numpy(sample["observation.tactile"]).astype(np.float32) + if tactile.shape[0] != 2: + raise ValueError(f"expected tactile [2,H,W], got {tactile.shape}") + tactile_vmax = args.tactile_vmax + if tactile_vmax is None: + tactile_vmax = max(float(np.nanmax(tactile)), 1.0e-6) + + figure, axes = plt.subplots(2, 3, figsize=(16, 9), constrained_layout=True) + for axis, (key, title) in zip(axes[0], RGB_KEYS, strict=True): + axis.imshow(rgb_hwc(sample[key]), interpolation="nearest") + axis.set_title(title) + axis.axis("off") + + depth_artist = axes[1, 0].imshow( + depth, cmap="cividis", vmin=0.0, vmax=args.depth_max_mm, + interpolation="nearest" + ) + axes[1, 0].set_title("Front depth (lossless uint16)") + axes[1, 0].axis("off") + figure.colorbar(depth_artist, ax=axes[1, 0], label="millimetres", fraction=0.046) + + tactile_artists = [] + for axis, values, title in zip( + axes[1, 1:], tactile, ("Left tactile", "Right tactile"), strict=True + ): + tactile_artists.append(axis.imshow( + values, cmap="magma", vmin=0.0, vmax=tactile_vmax, + interpolation="nearest", aspect="auto" + )) + axis.set_title(title) + axis.set_xlabel("sensor column") + axis.set_ylabel("sensor row") + figure.colorbar( + tactile_artists[-1], ax=list(axes[1, 1:]), label="calibrated response", + fraction=0.023 + ) + + state_tip = to_numpy(sample["observation.state_gripper"]) + action_tip = to_numpy(sample["action_gripper"]) + relative_time = float(to_numpy(sample["timestamp"]).reshape(-1)[0]) + figure.suptitle( + f"teleoperation/cyclically_arrange_steel_plate | episode {args.episode_index} | frame {frame_index} | " + f"t={relative_time:.3f}s\n" + f"tip xyz={np.array2string(state_tip[:3], precision=4)} m | " + f"target xyz={np.array2string(action_tip[:3], precision=4)} m" + ) + args.output.parent.mkdir(parents=True, exist_ok=True) + figure.savefig(args.output, dpi=140, facecolor="white") + plt.close(figure) + print(args.output.resolve()) + + +if __name__ == "__main__": + main() diff --git a/meta/episodes/chunk-000/file-000.parquet b/meta/episodes/chunk-000/file-000.parquet new file mode 100644 index 0000000000000000000000000000000000000000..94e7dfaaa440b5f850a683766b758b6a2a2bc970 --- /dev/null +++ b/meta/episodes/chunk-000/file-000.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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