Voxel51/AgiBotWorld2026-sample-10ep

This is a subset of the agibot-world/AgiBotWorld2026 dataset created for use with FiftyOne.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub("Voxel51/AgiBotWorld2026-sample-10ep")

session = fo.launch_app(dataset)

Dataset Card for AgiBot World 2026 (10-episode FiftyOne subset)

Dataset Details

Dataset Description

AgiBot World 2026 is a large-scale, real-world embodied intelligence dataset collected on the AgiBot G2 dual-arm humanoid platform. It spans three top-level splits on the Hugging Face repo — ImitationLearning, ReinforcementLearning, and RichInteraction (plus a separate simulation split from the companion GenieSim project) — covering commercial-space, home, and other general-purpose manipulation scenarios.

This subset contains 10 episodes taken from a single ReinforcementLearning shard (ReinforcementLearning/Home/task_12192/HG-DAgger/20857245_20857259.tar.gz) of the source repo, all for the task "Insert the loose change into the coin slot of the piggy bank dagger". Each episode records the dual-arm robot performing this insertion task under HG-DAgger-style intervention/rollout collection, with 7 synchronized camera streams and full proprioceptive state/action vectors.

• Curated by: AgiBot World Team (source data); FiftyOne subset prepared for browsing/exploration in FiftyOne.
• Funded by: Not specified in the source dataset.
• Language(s): English (task instruction text)
• License: CC BY-NC-SA 4.0 (inherited from the source dataset)

Dataset Sources

• Repository: https://huggingface.co/datasets/agibot-world/AgiBotWorld2026

Uses

Direct Use

Exploring, filtering, and visually inspecting AgiBot World 2026 robot-manipulation episodes in the FiftyOne App; prototyping data loaders and visualization pipelines for LeRobot-format multimodal robot-learning data before scaling up to the full 12+ TB source dataset; understanding the shape and structure of AgiBot's HG-DAgger reinforcement-learning collection format (reward/done/intervened/succeed/truncated per-frame signals).

Out-of-Scope Use

Not intended as a representative sample of the full AgiBot World 2026 dataset — it covers a single task, single collection mode (HG-DAgger), and single category (ReinforcementLearning) out of the many tasks/scenes/categories in the source. Not suitable for training production policies given its small size (10 episodes). Commercial use is restricted by the CC BY-NC-SA 4.0 license.

Dataset Structure

This dataset uses FiftyOne's multimodal / LeRobot representation: one sample = one episode. Each sample references the episode's per-frame Parquet data (state/action vectors) and its 7 per-camera MP4 video streams via a media_reference; there are no per-sample image/video fields directly on the sample.

Fields

Field	Type	Description
id	ObjectIdField	FiftyOne sample ID
media_reference	MediaReferenceField	Pointer to the episode's Parquet (state/action) and MP4 (camera) shard files
episode_index	IntField	Episode index within this subset (0-9)
task	StringField	Free-text task instruction for the episode (all episodes share the same instruction in this subset)
tasks	ListField(StringField)	List form of the task instruction(s) associated with the episode
length	IntField	Number of frames in the episode (2176-2582 in this subset)
duration	FloatField	Episode duration in seconds (72.5-86.1s in this subset)
robot_type	StringField	Robot platform, g2a (AgiBot G2) for all episodes
fps	FloatField	Frame rate, 30.0 for all episodes

task is a StringField rather than a Classification because the source stores it as free-text natural-language instructions, not a fixed label taxonomy.

Per-frame data is not materialized as FiftyOne fields — it is read on demand from the referenced Parquet/MP4 shards and surfaced in the FiftyOne App's State & Action / Streams tabs. The per-frame schema (from meta/info.json) is:

Per-frame feature	dtype / shape	Description
observation.images.top_head	video, 400x640x3	Head-mounted top camera (AV1/yuv420p)
observation.images.hand_left	video, 528x640x3	Left hand/wrist camera (AV1/yuv420p)
observation.images.hand_right	video, 528x640x3	Right hand/wrist camera (AV1/yuv420p)
observation.images.head_left_fisheye	video, 768x960x3	Head left fisheye camera (AV1/yuv420p)
observation.images.head_right_fisheye	video, 768x960x3	Head right fisheye camera (AV1/yuv420p)
observation.images.head_back_fisheye	video, 768x960x3	Head rear-facing fisheye camera (AV1/yuv420p)
observation.images.head_depth	video, 400x640x3, depth map	Head depth stream, encoded as PNG-codec video with gray16be pixel format
observation.state	float32[216]	Full proprioceptive state vector: effector positions, end-effector wrench/velocity/orientation/position, joint position/effort/mode/velocity, head/waist position & mode, robot base position/orientation, plus 6 camera extrinsic rotation matrices + translation vectors (see meta/info.json -> features.observation.state.field_descriptions for the exact index layout)
action	float32[44]	Action vector: effector positions, end-effector position/orientation, joint position, head/waist position, robot base velocity (see field_descriptions for index layout)
reward	float32[1]	Per-frame RL reward signal
done	bool[1]	Episode-termination flag
succeed	bool[1]	Task-success flag
intervened	bool[1]	Whether a human intervened at this frame (HG-DAgger collection signal)
truncated	bool[1]	Episode-truncation flag
action_prob	float32[1]	Policy action probability/confidence at this frame
episode_index, frame_index, index, task_index, timestamp	int64 / float32	Standard LeRobot bookkeeping columns

Label types and why

There are no detection/classification/segmentation label fields in this subset — it is raw multimodal sensorimotor data (video + proprioceptive state/action time series), not annotated with FiftyOne label types.

dataset.info contents

{
    "lerobot": {
        "format": "LeRobotDataset",
        "format_major": 3,
        "episode_count": 10,
        "imported_episode_count": 10,
        "skipped_episodes": [],
    }
}

Parsing decisions

• Why these episodes: The source agibot-world/AgiBotWorld2026 repo (12.3 TB, 420 files) is not laid out as a standard flat LeRobot repo — it has no root-level meta/info.json. Instead it ships as per-task .tar.gz archives nested under ImitationLearning/, ReinforcementLearning/, RichInteraction/, and simulation/, each containing a self-contained LeRobot v2.1 export (data/, meta/, videos/) for one or more episodes of one task. This subset uses all 10 episodes from a single archive, ReinforcementLearning/Home/task_12192/HG-DAgger/20857245_20857259.tar.gz, chosen because it was the smallest available ReinforcementLearning archive (~3.8 GB) and contains a complete, internally consistent set of episodes for one task.
• Why only ReinforcementLearning, not a mix of categories: ImitationLearning, ReinforcementLearning, and RichInteraction archives were sampled and inspected, but they declare incompatible per-frame schemas — observation.state is shape [193] for ImitationLearning, [216] for ReinforcementLearning (this subset), and [169] for RichInteraction, and only ReinforcementLearning episodes carry the reward/done/succeed/intervened/truncated/action_prob columns. A single LeRobot v3 export needs one consistent Parquet schema, so mixing categories was not possible; this subset stays within the single-schema ReinforcementLearning / HG-DAgger slice.
• Excluded modalities: none dropped from the declared feature set. observation.images.head_depth is declared as dtype: "video" (not the FiftyOne-importer-unsupported depth_video), so it imported like an RGB stream even though it is a gray16be PNG-codec depth map — no pixel values are log/linearly re-encoded and no decode parameters are needed, unlike some other LeRobot depth conventions.
• v2.1 -> v3.0 conversion: the source ships LeRobot v2.1. It was converted in place with the official lerobot.scripts.convert_dataset_v21_to_v30 (adding the total_chunks/total_videos/total_tasks fields the converter expects) before import. Extra top-level info.json keys not part of the LeRobot v2.1/v3 spec (camera_parameters, data_version, h5_path, instruction_segments, key_frame, take_over — AgiBot's additional annotation layers for subtask segments, 2D bounding boxes, and instruction-level step labels) were dropped by the converter and are not present in this FiftyOne dataset or its re-export.
• Re-export on push: FiftyOne's push_to_hub re-exports the 10 selected episodes as a fresh, self-contained LeRobot v3 repo — episode_index is remapped to 0..9, task_index is remapped to the single task present, and per-episode/global statistics are recomputed from the 10 episodes actually included (they are not copied from the much larger source dataset's statistics).
• Codec caveats: the 6 RGB/fisheye camera streams use AV1 (libdav1d/yuv420p), which decodes fine in Chromium-based browsers; the depth stream uses a PNG codec with gray16be, an unusual combination that may not render as a normal video in all players/browsers.
• Per-episode video statistics are zero: the source meta/episodes_stats.jsonl reports min/max/mean/std of 0.0 for every video feature — this is a known upstream artifact of AgiBot's stats pipeline (documented on the source dataset card as not affecting usage), not an error introduced by this subset.

Dataset Creation

Curation Rationale

Created to make a small, browsable, schema-consistent slice of the very large (12.3 TB) AgiBot World 2026 dataset available for exploration in FiftyOne, and to document the dataset's non-standard multi-archive repo layout and per-category schema differences for future subsetting work.

Source Data

Data Collection and Processing

Collected on the AgiBot G2 dual-arm robot platform in 100% real-world environments (commercial spaces, homes, and other general-purpose scenarios) via free-form teleoperation and, for the ReinforcementLearning category, an HG-DAgger-style human-in-the-loop intervention/rollout collection process. See the source dataset card for full collection methodology.

Who are the source data producers?

The AgiBot World Team.

Annotations

Annotation process

No additional annotations were added for this subset. The source dataset's extended annotation layers (subtask "Task Frame" segments, 2D bounding boxes, instruction-level "skill" segments, stored under key_frame/instruction_segments in the source meta/info.json) are not carried into this FiftyOne subset — see Parsing decisions.

Personal and Sensitive Information

Episodes are recorded in real-world commercial and home environments and may contain incidental appearances of people, personal spaces, or identifiable objects/branding. No explicit PII redaction is documented by the source dataset.

Citation

BibTeX:

@misc{agibotworld2026,
    title        = {AgiBot World 2026},
    author       = {AgiBot World Team},
    howpublished = {\url{https://huggingface.co/datasets/agibot-world/AgiBotWorld2026}},
    year         = {2026}
}

APA:

AgiBot World Team. (2026). AgiBot World 2026 [Data set]. Hugging Face. https://huggingface.co/datasets/agibot-world/AgiBotWorld2026

Dataset Card Authors

Harpreet Sahota

Dataset Card Contact

Harpreet Sahota