Datasets:

CaryxAI
/

everyday-manipulation-3d

@@ -1,213 +1,300 @@
 ---
-license: other
-license_name: cc-by-nc-sa-4.0-with-mano-carve-out
-license_link: https://huggingface.co/datasets/CaryxAI/everyday-manipulation-3d/blob/main/LICENSE.txt
-pretty_name: CaryX Everyday Manipulation 3D
-size_categories:
-- n<1K
 task_categories:
 - robotics
 tags:
 - LeRobot
-- egocentric
-- human-manipulation
-- rgbd
-- hand-pose
-- contact
-- metric-depth
-- lidar
-- segmentation
-- physical-ai
 ---
-# everyday-manipulation-3d
-**[Browse the episodes in your browser →](https://caryx.ai/data)** · [caryx.ai](https://caryx.ai) · [founders@caryx.ai](mailto:founders@caryx.ai)
-Every frame carries metric depth, 6-DoF camera pose, MANO hand pose,
-per-hand object contact, hand and object segmentation, and time-aligned
-language, so a model can be tested against any one of those channels or all of
-them at once without collecting or labelling anything first.
-Recorded on iPhone Pro (ARKit LiDAR) as part of Everyday Manipulation 1 (EM1),
-annotated by CaryX AI, packaged as a single multi-episode
-[LeRobot v3.0](https://github.com/huggingface/lerobot) dataset. This is a
-pilot-scale research corpus (32 episodes, 4 tasks, 2 participants): built for
-method development and per-channel evaluation, not for broad generalization
-claims.
-**116 episodes · 28019 frames @ 15 fps · release v0.4**
-| task | episodes | frames |
-|---|---:|---:|
-| `Wash the spoon with the sponge and rinse it clean.` | 1 | 565 |
-| `fold_cloth` | 1 | 455 |
-| `fold_laundry` | 12 | 3546 |
-| `jar_open_close` | 5 | 814 |
-| `pick_and_place` | 71 | 14999 |
-| `pour_liquid` | 1 | 323 |
-| `scoop_grain` | 11 | 4488 |
-| `sweep` | 12 | 2317 |
-| `tie_shoelaces` | 1 | 318 |
-| `unplug_charger` | 1 | 194 |
-## Quick start
-Requires Python 3.12+ and `lerobot==0.6.0` (the version this dataset was
-written and verified with).
-```python
-# pip install "lerobot==0.6.0"
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-# depth_output_unit: the loader default is MILLIMETRES; pass "m" for metres.
-ds = LeRobotDataset("CaryxAI/everyday-manipulation-3d", depth_output_unit="m")
-frame = ds[0]  # RGB, metric depth, MANO hands, contact, camera pose in one dict
 ```
-Storage shapes below are HWC (height, width, channel). The official loader
-returns image tensors as CHW floats in [0, 1], which is normal LeRobot
-behaviour.
-## License
-**Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC-BY-NC-SA-4.0)**, non-commercial.
-https://creativecommons.org/licenses/by-nc-sa/4.0/.
-MANO-derived data (`observation.mano_joints`, `observation.mano_params`, and the
-HaMeR hand fields in the episode sidecars) is subject to the
-[MANO license](https://mano.is.tue.mpg.de/license.html). Full terms:
-`LICENSE.txt`.
-## Episode orientation
-Most episodes were recorded portrait; a few were recorded landscape and are
-shipped ROTATED 90° CCW onto the same portrait canvas so the dataset merges
-under one schema. Those episodes are flagged in
-`meta/foundry.json` `episodes.<id>.raster_rotation = "ccw90"`: rotate their image
-channels 90° CW to display upright. All spatial channels (RGB, depth, masks,
-camera pose, MANO orientation) are expressed consistently in the shipped,
-rotated frame, so training and 3D geometry need no special-casing.
-## Features
-Shapes are numpy shapes, so `(7,)` is a flat 7-element vector.
-| key | shape | notes |
-|---|---|---|
-| `observation.images.ego` | (512, 384, 3) | RGB ego frame, portrait, ONE uniform downscale of the native 1440×1920 (no aspect distortion). Exactly 2× the depth grid: `ego[y, x]` and `depth[y//2, x//2]` are the same ray. |
-| `observation.images.depth` | (256, 192, 1) video | **metric metres**, native ARKit sceneDepth grid (never resampled), 12-bit log-HEVC (quantizer [0.1, 5.0] m in `meta/info.json`). Intrinsics for both grids: `episodes/<id>/depth_geometry.json`. |
-| `observation.state` | (7,) | `[x, y, z]` wrist position + `[rx, ry, rz]` hand rotation (axis-angle of the hand root) + grasp, metric WORLD frame. The state tracks ONE hand per frame: the right hand when visible, else the left. Position and rotation are zero-phase smoothed (see `meta/foundry.json` `state_smoothed`); the wrist source is named per episode in `state_source`. A zero row is a placeholder, not a pose: check `state_valid`. |
-| `observation.state_valid` | (1,) | 1.0 = the state row is a real measured pose; 0.0 = no derivable hand pose on this frame (the row is zeros). Mask state losses with this flag. 9448 of 28019 frames are placeholders. |
-| `observation.state_hand` | (1,) | which hand the state tracks this frame: 1.0 = right, 0.0 = left, -1.0 = none. The state follows one hand and can switch when the right hand is lost; the state jumps at a switch because it is a different hand. |
-| `action` | (7,) | `[dx, dy, dz]` world position delta + `[drx, dry, drz]` axis-angle of the RELATIVE rotation between consecutive frames + grasp. An action is a real delta only when `state_valid` is 1.0 on BOTH frame t and t+1 AND `state_hand` is the same on both; a validity gap or a hand switch is a zero-delta boundary row. Mask action losses accordingly. |
-| `observation.camera_pose` | (4, 4) | camera to world (metric), expressed in the SHIPPED raster frame (x = raster right, y = raster down, z = forward) and gravity-verified per episode at export. Composes directly with the intrinsics in `depth_geometry.json`. |
-| `observation.contact` | (2, 2) | The shipped contact channel. Rows are the left and right hand, column k is object slot k. Values are contact strength in [0, 1]. Contact against any object is the max over slots. |
-| `observation.contact_valid` | (2, 2) | 1.0 where that hand-to-object distance was actually measured on that frame, 0.0 where it could not be (hand out of frame, no depth surface, or no object in that slot). Single-object episodes ship slot 1 all-zero rather than a fabricated "measured apart". |
-| `observation.mano_joints` | (2, 21, 3) | MANO/HaMeR joints RE-ROOTED at the wrist: joint 0 is exactly the origin, axes in the shipped raster's camera orientation (`mano_frame` in `meta/foundry.json`). World placement for the hand `state_hand` names, when both are valid: `world = (R_state @ R_go.T) @ joints + state[0:3]`, where `R_state` is the rotation matrix of `state[3:6]` and `R_go` of that hand's `mano_params[0:3]`; both are shipped per frame. The untracked hand has no shipped world anchor. The primary channel is tip-smoothed (named per episode in `mano_joints_channel`); the raw fit is recoverable from `observation.mano_params`. |
-| `observation.mano_params` | (2, 58) | full MANO parameterization: global_orient(3) ⊕ pose(45) ⊕ betas(10) |
-| `observation.mano_valid` | (2,) | 1.0 where the MANO fit is valid for that hand/frame |
-| `observation.images.object_mask` | (512, 384, 3) video | R = object slot 0, B = slot 1 (two-object episodes), G unused. Threshold > 127 (video codecs are lossy). Exact full-res masks: `episodes/<id>/object_mask.npz`. |
-| `observation.images.hand_mask` | (512, 384, 3) video | R = LEFT hand, B = RIGHT hand. Same thresholding. Reviewer-corrected (shipped) channel. Exact full-res masks: `episodes/<id>/hand_mask.npz`. |
-## Timing
-The `timestamp` column is true source time: every 1/15 s tick of
-the source clip ships as one frame (nearest source frame; nothing is dropped),
-so `timestamp` in this dataset and the times in `language_spans.json` are the
-same clock. Frames with no derivable hand pose ship with a zero state and
-`state_valid = 0.0` instead of being removed. Per episode,
-`meta/foundry.json` `episodes.<id>.source_frame_indices` gives the source
-video frame behind each tick and `source_fps` the source frame rate, so exact
-source-frame timing is recoverable.
-## Per-episode sidecars
-Full-resolution annotation that does not fit the fixed frame schema rides
-alongside each episode in `episodes/<capture_id>/`.
-| file | present on | what it is |
-|---|---|---|
-| `openego_sidecar.json` | all | the release copy of the OpenEgo annotation. Internal production bookkeeping (review state, gate results, correction history, checkpoints, methodology notes) is removed; it is not the full internal record. What IS deliberately kept as consumer provenance: per-stream `annotator_params` reduced to `{producer_version, model}`, the transcript source tag, and the `hand_pose.source` / intrinsics source tags; these say which model produced each stream so a consumer can branch on provenance. Path fields refer only to files or features of THIS release (`observation.images.ego`, sibling npz) or are null; full-resolution sources are in the companion raw dataset. |
-| `language_spans.json` | all | dense sub-step action labels, in seconds on the same clock as `timestamp`. Each span carries `source` (`vlm` or `human`, where `human` means a reviewer wrote or corrected that label). Use them for language-conditioned / sub-goal training by joining on `timestamp`; the LeRobot `task` column stays the static goal. |
-| `depth_geometry.json` | all | the depth camera's own intrinsics and extrinsics |
-| `object_mask.npz` | all | exact full-resolution object masks (the video channel is lossy) |
-| `hand_mask.npz` | all | exact full-resolution hand masks, the reviewer-corrected shipped channel (left = slot 0, right = slot 1) |
-| `hand_object_contact.npz` | all | full attributed contact: per hand and object distances, strengths, grasp states |
-| `object_object_contact.npz` | multi-object (15 of 116) | symmetric object-to-object contact |
-| `contact_corrections.npz` | where a reviewer ruled | reviewer contact-window rulings, kept separate from the measurement |
-| `object_registry.json` | multi-object (15 of 116) | object id to human label and mask colour slot |
-Both contact files are keyed by the same `object_ids` as `object_mask.npz`.
-To resolve which object is slot k, read
-`episodes.<capture_id>.contact_slot_object_ids` and
-`contact_slot_object_labels` in `meta/foundry.json`. The same k indexes the
-`object_mask` video channel (R = slot 0, B = slot 1) and `object_mask.npz`
-`object_ids[k]`.
-## Episodes and tasks
-Tasks are native LeRobot tasks (`meta/tasks.parquet`); filter episodes by
-`task_index`. The episode index (id, task, length) is in `meta/episodes/`. The
-per-episode CaryX AI metadata lives in **`meta/foundry.json`** (orientation
-flags, state source, source frame indices, contact slot ids). The official
-LeRobot loader does not surface custom metadata; fetch that file directly.
-## What is exact and what is not
-| channel | fidelity |
-|---|---|
-| RGB | native 1440×1920 downscaled to 384×512, ONE uniform scale factor (no aspect distortion). The raw full-resolution clips are in the companion raw dataset below. |
-| depth | native 256×192 grid, 12-bit log-quantized video over [0.1, 5.0] m. Quantization error is under ~0.5 mm p99 (max ~0.9 mm), far below LiDAR sensor noise. Readings outside the range saturate. |
-| masks | video channels are codec-lossy at outlines (~0.03 to 0.04% of pixels); the exact masks ship as `object_mask.npz` and `hand_mask.npz` per episode. |
-| time | 15 fps time-based resampling of the source clip (nearest source frame per tick; worst-case timing jitter is half a source frame). No frames are dropped. |
-| state | zero-phase smoothed (positions and rotations); the raw fit is recoverable from `observation.mano_params` and the sidecars. |
-| stats | `meta/stats.json` is computed over every frame of the corpus, not a sample. |
-`meta/checksums.json` is the sha256 manifest of this release (it does not
-list itself).
-## Canonical split
-`meta/splits.json` freezes an episode-level train/val/test split. The rule is
-deterministic: within each task, episodes are ordered by capture time
-(`capture_id` is time-sortable); the last episode of each task is `test`, the
-second-to-last is `val`, and the rest are `train`. This is a chronological
-partition by episode, not a held-out participant, scene or session: both
-participants appear in all three splits.
-| split | episodes | frames |
-|---|---:|---:|
-| train | 106 | 25588 |
-| val | 5 | 1168 |
-| test | 5 | 1263 |
-Report results on `test`; tune on `val`.
-## Provenance
-Self-collected capture by CaryX AI, recorded by 2 consenting adults in private
-homes. Annotations are produced by CaryX AI's pipeline using third-party models
-and reviewed by humans.
 ## Citation
-```bibtex
-@misc{caryx2026egocentric,
-  title        = {everyday-manipulation-3d: Egocentric Human Manipulation with Metric Depth, Contact, and MANO Hands},
-  author       = {CaryX AI},
-  year         = {2026},
-  publisher    = {CaryX AI},
-  howpublished = {\url{https://caryx.ai}},
-  url          = {https://huggingface.co/datasets/CaryxAI/everyday-manipulation-3d},
-  note         = {Dataset. Available at \url{https://caryx.ai/data}}
-}
-```
-The raw full-resolution RGB-D clips these episodes were built from are
-published separately:
-[CaryxAI/everyday-manipulation-3d-raw](https://huggingface.co/datasets/CaryxAI/everyday-manipulation-3d-raw)
-(CC BY 4.0, no annotations).
-Produced by CaryX AI. Reach out: [founders@caryx.ai](mailto:founders@caryx.ai)
-· [caryx.ai](https://caryx.ai)

 ---
+license: apache-2.0
 task_categories:
 - robotics
 tags:
 - LeRobot
+configs:
+- config_name: default
+  data_files: data/*/*.parquet
 ---
+This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
+<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path=CaryxAI/everyday-manipulation-3d">
+<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
+<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/>
+</a>
+## Dataset Description
+- **Homepage:** [More Information Needed]
+- **Paper:** [More Information Needed]
+- **License:** apache-2.0
+## Dataset Structure
+[meta/info.json](meta/info.json):
+```json
+{
+    "codebase_version": "v3.0",
+    "fps": 15,
+    "features": {
+        "observation.images.ego": {
+            "dtype": "image",
+            "shape": [
+                512,
+                384,
+                3
+            ],
+            "names": [
+                "height",
+                "width",
+                "channel"
+            ]
+        },
+        "observation.state": {
+            "dtype": "float32",
+            "shape": [
+                7
+            ],
+            "names": [
+                "x",
+                "y",
+                "z",
+                "rx",
+                "ry",
+                "rz",
+                "grasp"
+            ]
+        },
+        "observation.state_valid": {
+            "dtype": "float32",
+            "shape": [
+                1
+            ],
+            "names": [
+                "valid"
+            ]
+        },
+        "observation.state_hand": {
+            "dtype": "float32",
+            "shape": [
+                1
+            ],
+            "names": [
+                "hand"
+            ]
+        },
+        "action": {
+            "dtype": "float32",
+            "shape": [
+                7
+            ],
+            "names": [
+                "dx",
+                "dy",
+                "dz",
+                "drx",
+                "dry",
+                "drz",
+                "grasp"
+            ]
+        },
+        "observation.camera_pose": {
+            "dtype": "float32",
+            "shape": [
+                4,
+                4
+            ]
+        },
+        "observation.contact": {
+            "dtype": "float32",
+            "shape": [
+                2,
+                2
+            ]
+        },
+        "observation.contact_valid": {
+            "dtype": "float32",
+            "shape": [
+                2,
+                2
+            ]
+        },
+        "observation.images.depth": {
+            "dtype": "video",
+            "shape": [
+                256,
+                192,
+                1
+            ],
+            "names": [
+                "height",
+                "width",
+                "channel"
+            ],
+            "info": {
+                "is_depth_map": true,
+                "depth_unit": "m",
+                "video.height": 256,
+                "video.width": 192,
+                "video.codec": "hevc",
+                "video.pix_fmt": "gray12le",
+                "video.fps": 15,
+                "video.channels": 1,
+                "has_audio": false,
+                "video.g": 2,
+                "video.crf": 30,
+                "video.preset": null,
+                "video.fast_decode": 0,
+                "video.video_backend": "pyav",
+                "video.extra_options": {
+                    "x265-params": "lossless=1"
+                },
+                "video.depth_min": 0.1,
+                "video.depth_max": 5.0,
+                "video.shift": 3.5,
+                "video.use_log": true
+            }
+        },
+        "observation.mano_joints": {
+            "dtype": "float32",
+            "shape": [
+                2,
+                21,
+                3
+            ]
+        },
+        "observation.mano_params": {
+            "dtype": "float32",
+            "shape": [
+                2,
+                58
+            ],
+            "names": [
+                "hand",
+                "global_orient(3)+pose(45)+betas(10)"
+            ]
+        },
+        "observation.mano_valid": {
+            "dtype": "float32",
+            "shape": [
+                2
+            ],
+            "names": [
+                "left",
+                "right"
+            ]
+        },
+        "observation.images.object_mask": {
+            "dtype": "video",
+            "shape": [
+                512,
+                384,
+                3
+            ],
+            "names": [
+                "height",
+                "width",
+                "channel"
+            ],
+            "info": {
+                "video.height": 512,
+                "video.width": 384,
+                "video.codec": "av1",
+                "video.pix_fmt": "yuv420p",
+                "video.fps": 15,
+                "video.channels": 3,
+                "has_audio": false,
+                "video.g": 2,
+                "video.crf": 30,
+                "video.preset": 12,
+                "video.fast_decode": 0,
+                "video.video_backend": "pyav",
+                "video.extra_options": {},
+                "is_depth_map": false
+            }
+        },
+        "observation.images.hand_mask": {
+            "dtype": "video",
+            "shape": [
+                512,
+                384,
+                3
+            ],
+            "names": [
+                "height",
+                "width",
+                "channel"
+            ],
+            "info": {
+                "video.height": 512,
+                "video.width": 384,
+                "video.codec": "av1",
+                "video.pix_fmt": "yuv420p",
+                "video.fps": 15,
+                "video.channels": 3,
+                "has_audio": false,
+                "video.g": 2,
+                "video.crf": 30,
+                "video.preset": 12,
+                "video.fast_decode": 0,
+                "video.video_backend": "pyav",
+                "video.extra_options": {},
+                "is_depth_map": false
+            }
+        },
+        "timestamp": {
+            "dtype": "float32",
+            "shape": [
+                1
+            ],
+            "names": null
+        },
+        "frame_index": {
+            "dtype": "int64",
+            "shape": [
+                1
+            ],
+            "names": null
+        },
+        "episode_index": {
+            "dtype": "int64",
+            "shape": [
+                1
+            ],
+            "names": null
+        },
+        "index": {
+            "dtype": "int64",
+            "shape": [
+                1
+            ],
+            "names": null
+        },
+        "task_index": {
+            "dtype": "int64",
+            "shape": [
+                1
+            ],
+            "names": null
+        }
+    },
+    "total_episodes": 116,
+    "total_frames": 28019,
+    "total_tasks": 10,
+    "chunks_size": 1000,
+    "data_files_size_in_mb": 100,
+    "video_files_size_in_mb": 200,
+    "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
+    "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
+    "robot_type": "human_ego",
+    "splits": {
+        "train": "0:116"
+    }
+}
 ```
 ## Citation
+**BibTeX:**
+```bibtex
+[More Information Needed]
+```