Upload dataset

README.md

@@ -0,0 +1,168 @@
+---
+license: cc-by-4.0
+task_categories:
+- robotics
+tags:
+- LeRobot
+- dexterous-manipulation
+- point-cloud
+- xarm7
+- leap-hand
+configs:
+- config_name: default
+  data_files: data/*/*.parquet
+---
+
+<div align="center">
+
+# DexMulti: Grasp + Open (sample efficiency)
+
+[![Website](https://img.shields.io/badge/Project-Website-1f6feb?style=for-the-badge&logo=githubpages&logoColor=white)](https://dexmulti.github.io/)
+[![Paper](https://img.shields.io/badge/arXiv-2603.11655-b31b1b?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2603.11655)
+[![Code](https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/Msornerrrr/dexmulti)
+
+<video src="https://huggingface.co/datasets/HJ666/dexmulti_grasp_open_sample_efficiency/resolve/main/media/demo.mp4" controls autoplay loop muted playsinline width="100%"></video>
+
+<sub>One teleoperated demonstration: the two RGB cameras (left); the robot, the segmented object clouds with hand contact, and the end-effector path coloured by skill (right).</sub>
+
+</div>
+
+Sample-efficiency demonstrations of the **Grasp + Open** task (paper: sample-efficiency experiments): 124 unique demonstrations recorded on 2026-01-28, from which seven training sets were drawn. The sets overlap (the same recording appears in several sets), so every recording is stored once and `dexmulti/splits.json` lists the episodes of each set.
+
+Paper: *Concurrent Prehensile and Nonprehensile Manipulation: A Practical Approach to Multi-Stage Dexterous Tasks*
+(IROS 2026, [arXiv:2603.11655](https://arxiv.org/abs/2603.11655)). Code: <https://github.com/Msornerrrr/dexmulti>. Full documentation (conventions, loading without LeRobot, npz conversion): <https://github.com/Msornerrrr/dexmulti/blob/main/docs/dataset.md>.
+
+**Task:** Grasp an object, push open a container lid, and place the object inside.
+
+| | |
+|---|---|
+| robot | xArm7 arm + LEAP Hand (right), teleoperated with DexCap |
+| cameras | 2x Intel RealSense L515 (RGB 640x480) |
+| episodes / frames | 124 / 19539 (length 106–202, mean 157.6) |
+| rate | 10 Hz |
+| format | LeRobot v3.0, videos H.264 (yuv420p) |
+
+## Features
+
+| feature | shape | description |
+|---|---|---|
+| `observation.images.cam1`, `observation.images.cam2` | 480x640x3 video | RGB of the two cameras |
+| `observation.state` | (29,) float32 | see below |
+| `action` | (22,) float32 | see below |
+| `observation.pointcloud.object` | (512, 3) float32 | segmented point cloud of the first (grasped) object, robot base frame [m] |
+| `observation.pointcloud.drawer` | (512, 3) float32 | segmented point cloud of the container (key name kept from the code base) |
+| `observation.pointcloud.hand` | (512, 3) float32 | points on the LEAP Hand surface from forward kinematics |
+| `observation.contact.object`, `observation.contact.drawer` | (512, 1) float32 | per-point hand contact score in [0, 1] of the matching cloud |
+| `observation.skill` | int64 | 0: not in a skill (approach / transition), 1: skill 1 (first object), 2: skill 2 (the container) |
+| `task` | string | language instruction (one per dataset) |
+
+`observation.state`:
+
+| index | source key | meaning |
+|---|---|---|
+| 0–5 | `eef_pose` | measured end-effector pose: x, y, z [m], roll, pitch, yaw [rad] (scipy Euler `xyz`) |
+| 6–12 | `arm_state` | xArm7 joint positions [rad] |
+| 13–28 | `hand_state` | LEAP Hand joint positions [rad] (LEAP ROS order: 4 per finger, thumb last) |
+
+`action` (what the robot was commanded at this step, 10 Hz):
+
+| index | source key | meaning |
+|---|---|---|
+| 0–5 | `arm_cmd` | commanded end-effector pose: x, y, z [m], roll, pitch, yaw [rad] |
+| 6–21 | `hand_cmd` | LEAP Hand joint commands [rad] |
+
+Element names are in `meta/info.json`.
+
+## Frames and conventions
+
+- All poses and point clouds are in the xArm7 base frame (`link_base`), metres. Orientations are scipy Euler
+  `"xyz"` angles in radians; roll is near ±π, so its plot wraps.
+- Point clouds come from the two depth cameras (fused, segmented, 512 points per object) and are the inputs used by
+  DexMulti. Depth images and masks are not included.
+- `observation.skill` is derived from the skill keyframes (`task1_start..task1_end` = 1,
+  `task2_start..task2_end` = 2, inclusive).
+  Where the two recorded ranges overlap, skill 1 takes precedence; frames of `task2_start..task2_end`
+  outside skill 1 are 2 even when `task2_start < task1_start`. The ranges overlap in 5 episode(s)
+  (`dexmulti/keyframe_overlaps.json`, with the resulting label runs; `set: null` = the keyframes behind
+  `observation.skill`, other entries = per-set keyframes), e.g. `session_20260128_211639` with keyframes
+  [34, 74, 74, 162] is labelled 0 on [0, 33], 1 on [34, 74], 2 on [75, 162]. The keyframe files are unchanged.
+- `timestamp` is `frame_index / 10`; the original wall-clock times are in `dexmulti/episodes.json`.
+
+## DexMulti extras (`dexmulti/`)
+
+Not part of the LeRobot schema; used by the DexMulti code base:
+
+- `episodes.json`: per `episode_index` the original recording name, length, source set and wall-clock times.
+- `keyframes/<episode>.json`: keyframes of the stored copy (used for `observation.skill`); `keyframes/<set>/<episode>.json`: keyframes as used by each set.
+- `set_clouds/<set>.npz`: each set's own keyframe clouds where they differ from the stored copy (see below).
+- `library/<set>.npz`: the skill library of each set (see Training sets below).
+- `keyframe_overlaps.json`: recordings whose two keyframe ranges overlap, with the resulting `observation.skill` runs.
+- `cameras.json`: intrinsics (fx, fy, cx, cy) and extrinsics `T_base_cam` (camera optical frame to robot base) of
+  both cameras; constant for the whole dataset.
+
+Load as DexMulti episodes (no `lerobot` or `torch` needed):
+
+```python
+from dexmulti.data.lerobot import LeRobotEpisodes
+ds = LeRobotEpisodes("<path or repo id>")
+ep = ds.episode(0)          # dict: keys of dexmulti/data/episode.py + skill
+kf = ds.keyframes(0)        # task1_start, task1_end, task2_start, task2_end
+```
+
+or with LeRobot: `LeRobotDataset("HJ666/dexmulti_grasp_open_sample_efficiency")`.
+
+## Training sets (splits)
+
+`dexmulti/splits.json` maps each set to `{"source": <original folder>, "episode_index": [...]}`.
+
+| set | original folder | demos | skill library |
+|---|---|---|---|
+| `quantity_50` | `grasp-open-coconut50` | 50 | as run |
+| `quantity_20` | `grasp-open-coconut20` | 20 | rebuilt |
+| `quantity_10` | `grasp-open-coconut10` | 10 | rebuilt |
+| `quantity_5` | `grasp-open-coconut5` | 5 | as run |
+| `diversity_11obj_x4` | `grasp-open-11*4` | 44 | as run |
+| `diversity_4obj_x11` | `grasp-open-4*11` | 44 | as run |
+| `diversity_7obj_x6+2` | `grasp-open-7*6+2` | 44 | as run |
+
+- `quantity_N`: N demonstrations with a single object (coconut); `quantity_5/10/20` are subsets of `quantity_50`.
+- `diversity_*`: 44 demonstrations with 11 objects x 4, 4 objects x 11, or 7 objects x 6 + 2.
+
+Each set was processed (segmentation, point-cloud fusion, contact maps) separately when it was recorded, so a
+recording that appears in several sets has identical robot data and images in every copy but slightly different
+point clouds / contact maps (the processing is stochastic). This dataset stores one copy per recording, chosen in
+the order quantity_50, quantity_20, quantity_10, quantity_5, diversity_11obj_x4, diversity_4obj_x11,
+diversity_7obj_x6+2 (`source_set` in `dexmulti/episodes.json`). The skill keyframes and skill libraries are kept per
+set exactly as they were used (`dexmulti/keyframes/<set>/`, `dexmulti/library/<set>.npz`); the keyframes of the
+same recording can differ slightly between sets. The libraries of `quantity_10` and `quantity_20` were not saved
+when the experiments ran and were rebuilt with `dexmulti.library.build_library` from those sets' own processed
+copies.
+
+A library is built from two clouds per demonstration: the object cloud at the set's `task1_start` and the
+second-object cloud at its `task2_start`. For the recordings whose stored copy comes from another set and differs at
+those two frames, `dexmulti/set_clouds/<set>.npz` holds that set's own clouds (and contact maps) at those frames
+(counts in `dexmulti/set_clouds/NOTES.json`). `LeRobotEpisodes(root, split="<set>")` returns the episodes of the set
+with its own keyframes (`skill` recomputed from them) and these clouds substituted, so materializing a set and
+rebuilding its library with `dexmulti.library.build_library` reproduces `dexmulti/library/<set>.npz` exactly, for
+all seven sets. All other frames are those of the stored copy.
+
+```python
+from dexmulti.data.lerobot import LeRobotEpisodes, materialize
+q5 = LeRobotEpisodes("<path or repo id>", split="quantity_5")
+materialize(q5, "data/npz/quantity_5")   # then: python scripts/build_library.py --session data/npz/quantity_5
+```
+
+## Citation
+
+```bibtex
+@inproceedings{jiang2026concurrent,
+  title     = {Concurrent Prehensile and Nonprehensile Manipulation: A Practical Approach to Multi-Stage Dexterous Tasks},
+  author    = {Jiang, Hao and Wu, Yue and Wang, Yue and Sukhatme, Gaurav S. and Seita, Daniel},
+  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
+  year      = {2026}
+}
+```
+
+## License
+
+[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). Please cite the paper when using the data.

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dexmulti/cameras.json

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+{
+ "convention": "fx_fy_cx_cy: pinhole intrinsics of the 640x480 color image; T_base_cam: 4x4 transform mapping points from the camera optical frame (OpenCV: x right, y down, z forward) to the robot base frame. Constant over all frames and episodes of this dataset.",
+ "cam1": {
+  "fx_fy_cx_cy": [
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+   597.4514770507812,
+   328.7528076171875,
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+   333.2942199707031,
+   245.56826782226562
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+ }
+}

dexmulti/keyframe_overlaps.json

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+[
+ {
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+  "set": null,
+  "keyframes": {
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+ },
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+ },
+ {
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+  "set": "diversity_4obj_x11",
+  "keyframes": {
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+   "task2_end": 165
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+ },
+ {
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+  "set": "diversity_7obj_x6+2",
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+   ]
+  ]
+ }
+]

dexmulti/keyframes/diversity_11obj_x4/session_20260128_204325.json

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+{
+ "episode": "session_20260128_204325",
+ "task1_start": 29,
+ "task1_end": 60,
+ "task1_dist_thr": 0.15,
+ "task1_radius_est": 0.12150227054953576,
+ "task1_lift_dz": 0.02110911726951599,
+ "task1_contact_key": "fused_segmented_point_cloud_base_object_to_hand_point_cloud_base_contact_map",
+ "task2_start": 65,
+ "task2_end": 156,
+ "task2_dist_thr": 0.12279527455568315,
+ "task2_radius_est": 0.09445790350437165,
+ "task2_contact_key": "fused_segmented_point_cloud_base_drawer_to_hand_point_cloud_base_contact_map"
+}

dexmulti/keyframes/diversity_11obj_x4/session_20260128_204420.json

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+{
+ "episode": "session_20260128_204420",
+ "task1_start": 36,
+ "task1_end": 77,
+ "task1_dist_thr": 0.15,
+ "task1_radius_est": 0.1201953336596489,
+ "task1_lift_dz": 0.02436538428068161,
+ "task1_contact_key": "fused_segmented_point_cloud_base_object_to_hand_point_cloud_base_contact_map",
+ "task2_start": 83,
+ "task2_end": 195,
+ "task2_dist_thr": 0.12167876273393632,
+ "task2_radius_est": 0.0935990482568741,
+ "task2_contact_key": "fused_segmented_point_cloud_base_drawer_to_hand_point_cloud_base_contact_map"
+}

dexmulti/keyframes/diversity_11obj_x4/session_20260128_204452.json

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+{
+ "episode": "session_20260128_204452",
+ "task1_start": 36,
+ "task1_end": 73,
+ "task1_dist_thr": 0.14725431881845,
+ "task1_radius_est": 0.11327255293726922,
+ "task1_lift_dz": 0.025584654361009596,
+ "task1_contact_key": "fused_segmented_point_cloud_base_object_to_hand_point_cloud_base_contact_map",
+ "task2_start": 108,
+ "task2_end": 192,
+ "task2_dist_thr": 0.12343453340232376,
+ "task2_radius_est": 0.09494964107871058,
+ "task2_contact_key": "fused_segmented_point_cloud_base_drawer_to_hand_point_cloud_base_contact_map"
+}

dexmulti/keyframes/diversity_11obj_x4/session_20260128_204554.json

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+{
+ "episode": "session_20260128_204554",
+ "task1_start": 31,
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dexmulti/keyframes/diversity_11obj_x4/session_20260128_212354.json

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+{
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dexmulti/keyframes/diversity_11obj_x4/session_20260128_212423.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_212459.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_212550.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_212929.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_212956.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_213047.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_213116.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_213222.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_213303.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_213327.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_213356.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_213438.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_213503.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_213603.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_213633.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_213902.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_213943.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_214012.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_214040.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_214155.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_214300.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_214331.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_214355.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_214448.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_214515.json

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dexmulti/keyframes/diversity_11obj_x4/session_20260128_214539.json

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+}

dexmulti/keyframes/diversity_11obj_x4/session_20260128_214611.json

@@ -0,0 +1,14 @@
+{
+ "episode": "session_20260128_214611",
+ "task1_start": 37,
+ "task1_end": 66,
+ "task1_dist_thr": 0.04737902112305167,
+ "task1_radius_est": 0.0364454008638859,
+ "task1_lift_dz": 0.02,
+ "task1_contact_key": "fused_segmented_point_cloud_base_object_to_hand_point_cloud_base_contact_map",
+ "task2_start": 84,
+ "task2_end": 142,
+ "task2_dist_thr": 0.12003581561148169,
+ "task2_radius_est": 0.09233524277806283,
+ "task2_contact_key": "fused_segmented_point_cloud_base_drawer_to_hand_point_cloud_base_contact_map"
+}

dexmulti/keyframes/diversity_11obj_x4/session_20260128_214646.json

@@ -0,0 +1,14 @@
+{
+ "episode": "session_20260128_214646",
+ "task1_start": 27,
+ "task1_end": 54,
+ "task1_dist_thr": 0.08806331150233747,
+ "task1_radius_est": 0.0677410088479519,
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+ "task1_contact_key": "fused_segmented_point_cloud_base_object_to_hand_point_cloud_base_contact_map",
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+ "task2_end": 123,
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+ "task2_radius_est": 0.09382654875516892,
+ "task2_contact_key": "fused_segmented_point_cloud_base_drawer_to_hand_point_cloud_base_contact_map"
+}

dexmulti/keyframes/diversity_11obj_x4/session_20260128_214748.json

@@ -0,0 +1,14 @@
+{
+ "episode": "session_20260128_214748",
+ "task1_start": 24,
+ "task1_end": 63,
+ "task1_dist_thr": 0.09180491372942925,
+ "task1_radius_est": 0.07061916440725327,
+ "task1_lift_dz": 0.02134030491113663,
+ "task1_contact_key": "fused_segmented_point_cloud_base_object_to_hand_point_cloud_base_contact_map",
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+ "task2_radius_est": 0.0949399083852768,
+ "task2_contact_key": "fused_segmented_point_cloud_base_drawer_to_hand_point_cloud_base_contact_map"
+}

dexmulti/keyframes/diversity_11obj_x4/session_20260128_214839.json

@@ -0,0 +1,14 @@
+{
+ "episode": "session_20260128_214839",
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+ "task1_end": 72,
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+ "task1_lift_dz": 0.03494764789938926,
+ "task1_contact_key": "fused_segmented_point_cloud_base_object_to_hand_point_cloud_base_contact_map",
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+ "task2_dist_thr": 0.12242599062621595,
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+ "task2_contact_key": "fused_segmented_point_cloud_base_drawer_to_hand_point_cloud_base_contact_map"
+}

dexmulti/keyframes/diversity_11obj_x4/session_20260128_214911.json

@@ -0,0 +1,14 @@
+{
+ "episode": "session_20260128_214911",
+ "task1_start": 37,
+ "task1_end": 71,
+ "task1_dist_thr": 0.08866251781582833,
+ "task1_radius_est": 0.0682019367814064,
+ "task1_lift_dz": 0.02154617518186569,
+ "task1_contact_key": "fused_segmented_point_cloud_base_object_to_hand_point_cloud_base_contact_map",
+ "task2_start": 93,
+ "task2_end": 150,
+ "task2_dist_thr": 0.12110948152840138,
+ "task2_radius_est": 0.09316113963723183,
+ "task2_contact_key": "fused_segmented_point_cloud_base_drawer_to_hand_point_cloud_base_contact_map"
+}

dexmulti/keyframes/diversity_11obj_x4/session_20260128_215110.json

@@ -0,0 +1,14 @@
+{
+ "episode": "session_20260128_215110",
+ "task1_start": 28,
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+ "task1_lift_dz": 0.02,
+ "task1_contact_key": "fused_segmented_point_cloud_base_object_to_hand_point_cloud_base_contact_map",
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+ "task2_contact_key": "fused_segmented_point_cloud_base_drawer_to_hand_point_cloud_base_contact_map"
+}

dexmulti/keyframes/diversity_11obj_x4/session_20260128_215139.json

@@ -0,0 +1,14 @@
+{
+ "episode": "session_20260128_215139",
+ "task1_start": 40,
+ "task1_end": 76,
+ "task1_dist_thr": 0.06659087263047697,
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+ "task1_contact_key": "fused_segmented_point_cloud_base_object_to_hand_point_cloud_base_contact_map",
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+ "task2_end": 150,
+ "task2_dist_thr": 0.12086142838001251,
+ "task2_radius_est": 0.09297032952308655,
+ "task2_contact_key": "fused_segmented_point_cloud_base_drawer_to_hand_point_cloud_base_contact_map"
+}

dexmulti/keyframes/diversity_11obj_x4/session_20260128_215208.json

@@ -0,0 +1,14 @@
+{
+ "episode": "session_20260128_215208",
+ "task1_start": 50,
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+ "task1_lift_dz": 0.020216270238161086,
+ "task1_contact_key": "fused_segmented_point_cloud_base_object_to_hand_point_cloud_base_contact_map",
+ "task2_start": 115,
+ "task2_end": 188,
+ "task2_dist_thr": 0.12066221855580807,
+ "task2_radius_est": 0.09281709119677543,
+ "task2_contact_key": "fused_segmented_point_cloud_base_drawer_to_hand_point_cloud_base_contact_map"
+}