--- license: other license_name: microagi-os-l1 license_link: LICENSE task_categories: - robotics language: - en tags: - dataset - egocentric - robotics - rgbd - depth - manipulation - mcap - ros2 - computer_vision pretty_name: MicroAGI00 Egocentric Dataset for Simple Household Manipulation size_categories: - 1M License: MicroAGI00 Open Use, No-Resale v1.0 (see `LICENSE`). > No resale: You may not sell or paywall this dataset or derivative data. Trained models/outputs may be released under any terms. ## Overview MicroAGI00 is a large-scale egocentric RGB+D dataset of human manipulation in https://behavior.stanford.edu/challenge/index.html tasks. ## Quick facts * Modality: synchronized RGB + 16‑bit depth + IMU + annotations * Resolution & rate (RGB): 1920×1080 @ 30 FPS (in MCAP) * Depth: 16‑bit, losslessly compressed inside MCAP * Scale: ≈1,000,000 synchronized RGB frames and ≈1,000,000 depth frames (≈1M frame pairs) * Container: `.mcap` (all signals + annotations) * Previews: For as sample for only some bags `.mp4` per sequence (annotated RGB; visualized native depth) * Annotations: Only in %5 of the dataset, hand landmarks and short action text ## What’s included per sequence * One large **MCAP** file containing: * RGB frames (1080p/30 fps) * 16‑bit depth stream (lossless compression) * IMU data (as available) * For Some Data the Embedded annotations (hands, action text) **MP4** preview videos: * Annotated RGB (for quick review) * Visualized native depth map (for quick review) > Note: MP4 previews may be lower quality than MCAP due to compression and post‑processing. Research use should read from MCAP. ## Annotations Annotations are generated by our in‑house. ### Hand annotations 21 Joints (not all shown below as it would be too long) (per frame) — JSON schema example ``` { "frame_number": 9, "timestamp_seconds": 0.3, "resolution": { "width": 1920, "height": 1080 }, "hands": [ { "hand_index": 0, "landmarks": [ { "id": 0, "name": "WRIST", "x": 0.7124036550521851, "y": 0.7347621917724609, "z": -1.444301744868426e-07, "visibility": 0.0 }, ], "hand": "Left", "confidence": 0.9268525838851929 }, { "hand_index": 1, "landmarks": [ { "id": 0, "name": "WRIST", "x": 0.4461262822151184, "y": 0.35183972120285034, "z": -1.2342320587777067e-07, "visibility": 0.0 }, "hand": "Right", "confidence": 0.908446729183197 } ], "frame_idx": 9, "exact_frame_timestamp": 1758122341583104000, "exact_frame_timestamp_sec": 1758122341.583104 } ``` ### Text (action) annotations (per frame/window) — JSON schema example ``` { "schema_version": "v1.0", "action_text": "Right hand, holding a knife, is chopping cooked meat held by the left hand on the red cutting board.", "confidence": 1.0, "source": { "model": "MicroAGI, MAGI01" }, "exact_frame_timestamp": 1758122341583104000, "exact_frame_timestamp_sec": 1758122341.583104 } ``` ## Data access and structure * Each top-level sample folder contains: One folder of strong heavy mcap dump, one folder of annotated mcap dump, one folder of mp4 previews * All authoritative signals and annotations are inside the MCAP. Use the MP4s for quick visual QA only. ## Getting started * Inspect an MCAP: `mcap info your_sequence.mcap` * Extract messages: `mcap cat --topics your_sequence.mcap > out.bin` * Python readers: `pip install mcap` (see the MCAP Python docs) or any MCAP-compatible tooling. Typical topics include RGB, depth, IMU, and annotation channels. ## Intended uses * Policy and skill learning (robotics/VLA) * Action detection and segmentation * Hand/pose estimation and grasp analysis * Depth-based reconstruction, SLAM, scene understanding * World-model pre-post training ## Services and custom data MicroAGI provides on-demand: * Real‑to‑Sim pipelines * ML‑enhanced 3D point clouds and SLAM reconstructions * New data capture via our network of skilled tradespeople and factory workers (often below typical market cost) * Enablement for your workforce to wear our device and run through our processing pipeline Typical lead times: under two weeks (up to four weeks for large jobs). ## How to order more Email `data@micro-agi.com` with: * Task description * Desired hours or frame counts * Proposed price We will reply within one business day with lead time and final pricing. Questions: `info@micro-agi.com` ## License This dataset is released under the MicroAGI00 Open Use, No‑Resale License v1.0 (custom). See [`LICENSE`](./LICENSE). Redistribution must be free‑of‑charge under the same license. Required credit: "This work uses the MicroAGI00 dataset (MicroAGI, 2025)." ## Attribution reminder Public uses of the Dataset or Derivative Data must include the credit line above in a reasonable location for the medium (papers, repos, product docs, dataset pages, demo descriptions). Attribution is appreciated but not required for Trained Models or Outputs.