Update README.md

README.md

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----
-license: other
-license_name: microagi-os-l1
-license_link: LICENSE
----
+---
+license: other
+license_name: microagi-os-l1
+license_link: LICENSE
+task_categories:
+- text-classification
+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<n<10M
+---
+---
+
+
+
+# MicroAGI00: MicroAGI Egocentric Dataset (2025)
+
+> Required credit: "This work uses the MicroAGI00 dataset (MicroAGI, 2025)."
+> 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 (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 <topic> 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 pretraining
+
+## 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.