README.md

---
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.

**32 episodes · 9161 frames @ 15 fps · release v0.4**

| task | episodes | frames |
|---|---:|---:|
| `fold_laundry` | 9 | 2387 |
| `jar_open_close` | 3 | 488 |
| `scoop_grain` | 11 | 4488 |
| `sweep` | 9 | 1798 |

## 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. 2296 of 9161 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 (12 of 32) | 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 (12 of 32) | 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 | 24 | 7117 |
| val | 4 | 973 |
| test | 4 | 1071 |

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)