Datasets:

Voxel51
/

egodex-test-multimodal

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type:  (... 524 chars omitted)
  child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 512 chars omitted)
      child 0, _id: struct<$oid: string>
          child 0, $oid: string
      child 1, filepath: string
      child 2, tags: list<item: null>
          child 0, item: null
      child 3, _media_type: string
      child 4, _rand: double
      child 5, task: string
      child 6, task_type: string
      child 7, episode_index: int64
      child 8, language: struct<_id: struct<$oid: string>, _cls: string, tags: list<item: null>, label: string>
          child 0, _id: struct<$oid: string>
              child 0, $oid: string
          child 1, _cls: string
          child 2, tags: list<item: null>
              child 0, item: null
          child 3, label: string
      child 9, language2: string
      child 10, objects: list<item: string>
          child 0, item: string
      child 11, verbs: list<item: string>
          child 0, item: string
      child 12, environment: string
      child 13, duration_s: double
      child 14, frame_count: int64
      child 15, message_count: int64
      child 16, has_video: bool
      child 17, has_skeleton: bool
      child 18, has_calibration: bool
      child 19, hdf5_path: string
      child 20, _dataset_id: struct<$oid: string>
          child 0, $oid: string
      chi
...
 string, subfield: string, fields:  (... 320 chars omitted)
  child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item:  (... 308 chars omitted)
      child 0, name: string
      child 1, ftype: string
      child 2, embedded_doc_type: string
      child 3, subfield: string
      child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: string, fields: li (... 117 chars omitted)
          child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: string, fields: list<item: nu (... 105 chars omitted)
              child 0, name: string
              child 1, ftype: string
              child 2, embedded_doc_type: null
              child 3, subfield: string
              child 4, fields: list<item: null>
                  child 0, item: null
              child 5, db_field: string
              child 6, description: null
              child 7, info: null
              child 8, read_only: bool
              child 9, created_at: struct<$date: string>
                  child 0, $date: string
      child 5, db_field: string
      child 6, description: null
      child 7, info: null
      child 8, read_only: bool
      child 9, created_at: struct<$date: string>
          child 0, $date: string
media_type: string
workspaces: list<item: null>
  child 0, item: null
last_loaded_at: struct<$date: string>
  child 0, $date: string
last_deletion_at: null
slug: string
to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('string'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type:  (... 524 chars omitted)
                child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 512 chars omitted)
                    child 0, _id: struct<$oid: string>
                        child 0, $oid: string
                    child 1, filepath: string
                    child 2, tags: list<item: null>
                        child 0, item: null
                    child 3, _media_type: string
                    child 4, _rand: double
                    child 5, task: string
                    child 6, task_type: string
                    child 7, episode_index: int64
                    child 8, language: struct<_id: struct<$oid: string>, _cls: string, tags: list<item: null>, label: string>
                        child 0, _id: struct<$oid: string>
                            child 0, $oid: string
                        child 1, _cls: string
                        child 2, tags: list<item: null>
                            child 0, item: null
                        child 3, label: string
                    child 9, language2: string
                    child 10, objects: list<item: string>
                        child 0, item: string
                    child 11, verbs: list<item: string>
                        child 0, item: string
                    child 12, environment: string
                    child 13, duration_s: double
                    child 14, frame_count: int64
                    child 15, message_count: int64
                    child 16, has_video: bool
                    child 17, has_skeleton: bool
                    child 18, has_calibration: bool
                    child 19, hdf5_path: string
                    child 20, _dataset_id: struct<$oid: string>
                        child 0, $oid: string
                    chi
              ...
               string, subfield: string, fields:  (... 320 chars omitted)
                child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item:  (... 308 chars omitted)
                    child 0, name: string
                    child 1, ftype: string
                    child 2, embedded_doc_type: string
                    child 3, subfield: string
                    child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: string, fields: li (... 117 chars omitted)
                        child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: string, fields: list<item: nu (... 105 chars omitted)
                            child 0, name: string
                            child 1, ftype: string
                            child 2, embedded_doc_type: null
                            child 3, subfield: string
                            child 4, fields: list<item: null>
                                child 0, item: null
                            child 5, db_field: string
                            child 6, description: null
                            child 7, info: null
                            child 8, read_only: bool
                            child 9, created_at: struct<$date: string>
                                child 0, $date: string
                    child 5, db_field: string
                    child 6, description: null
                    child 7, info: null
                    child 8, read_only: bool
                    child 9, created_at: struct<$date: string>
                        child 0, $date: string
              media_type: string
              workspaces: list<item: null>
                child 0, item: null
              last_loaded_at: struct<$date: string>
                child 0, $date: string
              last_deletion_at: null
              slug: string
              to
              {'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('string'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
              because column names don't match

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Dataset Card for EgoDex Test Set (FiftyOne Multimodal)

EgoDex is the largest and most diverse dataset of egocentric dexterous manipulation to date, published at ICLR 2026. Collected with Apple Vision Pro and ARKit, each episode pairs 1920×1080 30 Hz egocentric video with dense 3D skeletal pose annotations (SE(3) transforms for 68 body and finger joints), GPT-4 language descriptions, and camera extrinsics.

This FiftyOne dataset contains the test set (3,243 episodes across 111 tabletop manipulation tasks), converted to MCAP format for synchronized multimodal exploration in the FiftyOne App. Each MCAP episode streams H.264 video, a 3D animated skeleton, a 2D skeleton overlay projected onto the video frame, and per-episode metadata—all on a shared 30 Hz timeline.

Installation

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

dataset = load_from_hub("Voxel51/egodex-test-multimodal")
session = fo.launch_app(dataset)

Dataset Details

Dataset Sources

Repository: https://github.com/apple/ml-egodex
Paper: https://arxiv.org/abs/2505.11709
Demo: https://huggingface.co/spaces/harpreetsahota/egodex-fiftyone

Uses

Direct Use

Dexterous manipulation research: Pretraining and evaluating imitation learning policies for hand trajectory prediction. The paper's benchmark evaluates best-of-K Euclidean distance for 48-dimensional action chunks (wrist positions + 6D orientations + fingertip positions) over 1–3 second horizons.
Egocentric video understanding: Action recognition, human-object interaction detection, affordance learning, and object contact point modeling.
Video generation and world modeling: Language-conditioned egocentric video synthesis and world model training from the paired video, pose, and language modalities.
Robotics pretraining: Co-training with small-scale robot datasets; learning manipulation priors from human-object interaction trajectories for downstream fine-tuning.
FiftyOne multimodal exploration: Browse, filter, and compare episodes in the App across tasks, task types, duration, and language descriptions. The 3D panel renders the animated skeleton; the Image panel shows the 2D skeleton overlay directly on the video.

Out-of-Scope Use

This dataset contains only the test set (3,243 episodes, 7 hours). Training models from scratch requires the full training set (725 hours, available via the source repository). The dataset covers tabletop manipulation in constrained studio environments; use cases requiring scene diversity or outdoor settings are out of scope. Commercial use is excluded by the CC-BY-NC-ND license.

Dataset Structure

Overview

The FiftyOne dataset is flat with media_type="multimodal". Each sample corresponds to one manipulation episode stored as a single .mcap file. There are 3,243 samples covering 111 distinct tasks.

Episodes are organized by task (task field). No explicit train/val/test splits exist within this dataset — the entire dataset is the held-out 1% test partition from the paper's 99/1 train/test split.

Sample Fields

Field	FiftyOne type	Description
`filepath`	`StringField`	Absolute path to the `.mcap` file for this episode
`task`	`StringField`	Canonical task name using underscores (e.g., `basic_pick_place`)
`task_type`	`StringField`	Task category: `reset`, `reset-free`, or `reversible`
`episode_index`	`IntField`	Numeric index of the episode within its task folder
`language`	`Classification`	GPT-4-generated natural language description of the manipulation. For reversible tasks, the applicable direction is pre-selected using `which_llm_description`.
`language2`	`StringField`	Inverse-direction description for reversible tasks; empty string for non-reversible episodes
`objects`	`ListField(StringField)`	Manipulated objects identified by GPT-4 (e.g., `["stapler"]`)
`verbs`	`ListField(StringField)`	Action verbs identified by GPT-4 (e.g., `["pick", "place"]`)
`environment`	`StringField`	Structured string with table material, position, background, and hand used
`duration_s`	`FloatField`	Episode duration in seconds (range: ~1.7 s to 30+ s)
`frame_count`	`IntField`	Number of 30 Hz frames; equals `round(duration_s * 30)`
`message_count`	`IntField`	Total MCAP messages across all channels (≈ `frame_count × 4 + 2`)
`has_video`	`BooleanField`	True for all episodes (`foxglove.CompressedVideo` present)
`has_skeleton`	`BooleanField`	True for all episodes (`foxglove.SceneUpdate` skeleton present)
`has_calibration`	`BooleanField`	True for all episodes (`foxglove.CameraCalibration` present)
`hdf5_path`	`StringField`	Path to the source HDF5 file; used by training pipelines that need raw SE(3) data

MCAP Channels (per episode)

Each .mcap file contains six synchronized channels at 30 Hz (except calibration and metadata, which are logged once):

Topic	Schema	Rate	Description
`/camera/image_raw`	`foxglove.CompressedVideo`	30 Hz	H.264 encoded egocentric video, 1920×1080. Linked to calibration via `mcap.calibration_topic` metadata.
`/camera/calibration`	`foxglove.CameraCalibration`	Once at t₀	Constant pinhole intrinsics: fx=fy=736.6339, cx=960, cy=540. Distortion model `plumb_bob` with D=[0,0,0,0,0] (ARKit output is undistorted).
`/skeleton`	`foxglove.SceneUpdate`	30 Hz	3D animated skeleton as a `LineList` of bone segments in the ARKit world frame. Colors follow the devkit convention: index=tomato, middle=pale turquoise, ring=green-yellow, little=light blue, thumb=violet, arm/spine=grey.
`/skeleton_2d`	`foxglove.ImageAnnotations`	30 Hz	2D skeleton projected onto the image plane using `inv(cam_ext) @ joint_tf` then pinhole projection with K. `PointsAnnotation` with `LineList` type; same per-finger color scheme as `/skeleton`.
`/tf`	`foxglove.FrameTransform`	30 Hz	Dynamic camera extrinsics (parent=`world`, child=`camera`) extracted from the ARKit `transforms/camera` field. All 68 body joint positions are encoded in `/skeleton` and `/skeleton_2d` rather than as individual TF frames.
`/metadata`	`egodex_metadata` (JSON)	Once at t₀	Task name, task type, language description, objects, verbs, environment string, session name, annotator version, and collector code.

3D Coordinate Frame

All transforms originate from ARKit's stationary origin frame set at the start of each recording session. The world frame is not globally consistent across episodes — each session initializes its own origin. For training, poses should be converted to the camera frame using inv(cam_ext) @ joint_tf before computing action representations, as done in the paper's benchmark.

Source Annotations (in raw HDF5, accessible via `hdf5_path`)

The raw HDF5 files (not stored in this FiftyOne dataset) contain:

transforms/<joint_name> (N×4×4 float32): SE(3) pose in ARKit world frame for all 68 joints plus the camera. Joints: 20 upper body (hip, spine1–7, neck1–4, left/right shoulder/arm/forearm/hand), 24 left finger joints, 24 right finger joints.
confidences/<joint_name> (N float32): ARKit tracking confidence 0–1. Wrist confidence = whole-hand detection; finger confidence = relative to wrist. Absent in ~6% of episodes.
camera/intrinsic (3×3 float32): Constant across all files.
HDF5 attributes: session_name, environment, extra (collector ID), annotator_version, llm_description, llm_description2, llm_type, which_llm_description, llm_objects, llm_verbs.

Parsing Decisions

Reversible-task language: For reversible episodes, which_llm_description ('1' or '2') determines which of llm_description/llm_description2 applies to that episode. The applicable description is stored in language; the inverse is stored in language2.
Session name normalization: Raw session_name attributes contained several malformed variants (double .mov extension, all-dash separators, 5-digit years, trailing characters). A regex extractor handles all observed variants; ~29 of 3,243 episodes were affected.
Video transcoding: Source MP4 uses MPEG-4 Part 2 codec. Each episode is transcoded to H.264 (libx264, -bf 0, -g 30, level 5.0) before MCAP authoring to satisfy foxglove.CompressedVideo requirements.
Timestamps: HDF5 files contain no per-frame timestamps. Frame timestamps are synthesized as session_start_ns + frame_index × (1e9/30), where session_start_ns is parsed from session_name.

Dataset Creation

Curation Rationale

Imitation learning for robot manipulation is bottlenecked by data scarcity. Unlike text or images, there is no Internet-scale corpus of dexterous manipulation data. EgoDex addresses this by exploiting the passive scalability of egocentric human video: data is a byproduct of performing everyday tasks while wearing an Apple Vision Pro, rather than requiring deliberate robot operation. The dataset prioritizes behavioral diversity — 194 tasks covering a wide range of manipulation behaviors beyond pick-and-place — over scene diversity. ARKit's on-device SLAM and multi-camera calibration provide precise per-joint pose annotations without post-hoc estimation, which is critical for fine-grained dexterous manipulation data.

Source Data

Data Collection and Processing

All data is collected with Apple Vision Pro running visionOS 2, using ARKit for production-grade pose tracking. Data is recorded in sessions of approximately 10–15 minutes, each containing many individual episodes demarcated by pause/resume of the recording app. Raw video is compressed using modern video codecs (the raw data would otherwise exceed 500 TB). Language metadata is collected per-session by data collectors and then processed through GPT-4 to produce structured, consistent natural language descriptions.

Who are the source data producers?

Data collectors at Apple performed demonstrations of tabletop manipulation tasks while wearing Apple Vision Pro. No further information about individual collectors is publicly available.

Annotations

Annotation process

3D skeletal pose annotations are produced automatically at recording time by ARKit running on-device, using multiple calibrated cameras and on-device SLAM. This is a production-grade system without manual annotation. Language annotations are produced in two stages: (1) data collectors write brief free-text task descriptions; (2) GPT-4 synthesizes collector notes (task name, environment details, object descriptions) into a single polished natural language description per episode.

Who are the annotators?

Pose annotations are machine-generated by ARKit. Language annotations are generated by GPT-4 from collector-written metadata; the collectors are Apple employees.

Personal and Sensitive Information

The dataset does not contain faces, voices, or other biometric identifiers beyond hand and arm motion. The Apple Vision Pro captures only tabletop scenes from a first-person viewpoint; backgrounds are controlled studio environments. No personally identifiable information is included in the released data.

Citation

BibTeX:

@inproceedings{egodex,
  title     = {EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video},
  author    = {Ryan Hoque and Peide Huang and David J. Yoon and Mouli Sivapurapu and Jian Zhang},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2026},
  url       = {https://arxiv.org/abs/2505.11709}
}

APA:

Hoque, R., Huang, P., Yoon, D. J., Sivapurapu, M., & Zhang, J. (2026). EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video. International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2505.11709