Update README.md

README.md

@@ -1,141 +1,123 @@
----
-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<n<10M
----
----
-
 
 
 # MicroAGI00: MicroAGI Egocentric Dataset (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.
+> **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. 
+## What this is
 
+MicroAGI00 is a large-scale **egocentric RGB+D** dataset of **human household manipulation**, aligned with the task style of the Stanford BEHAVIOR benchmark: [https://behavior.stanford.edu/challenge/index.html](https://behavior.stanford.edu/challenge/index.html)
 
+It’s designed to be “robotics-ready” at the *signal level*: synchronized streams, clean packaging, strong QC, and consistent structure—so you can spend time modeling, not cleaning data.
 
 ## 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
+* **Modalities:** synchronized RGB + 16-bit depth + IMU
+* **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)
+* **Previews:** for a subset of sequences, `.mp4` previews (annotated overlays / visualized depth for quick review)
+
+> Note: MP4 previews may be lower quality than MCAP due to compression and post-processing. Research use should read from MCAP.
 
 ## What’s included per sequence
 
 * One large **MCAP** file containing:
 
   * RGB frames (1080p/30 fps)
-  * 16‑bit depth stream (lossless compression)
+  * 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
-}
-```
+
+* **MP4 preview videos** (subset of sequences):
+
+  * RGB preview (for quick visual QA)
+  * Visualized depth preview (for quick visual QA)
+
+## Labels / annotations
+
+The base MicroAGI00 release is **primarily raw synchronized signals** (RGB-D-IMU) and **does not ship with full-coverage labels**.
+
+If you’ve seen demo videos with overlays: those demonstrate **what MicroAGI can produce** as an add-on (see below), not what is universally present in the base dump.
+
+## Data quality and QC philosophy
+
+MicroAGI00 is built around *trustworthy signal integrity*:
+
+* Tight **RGB↔Depth synchronization** checks
+* Automated detection and scoring of:
+
+  * frame drops / time discontinuities
+  * motion blur / exposure failures
+  * depth sanity (range/invalid ratios), compression integrity
+  * IMU continuity where available
+* Consistent trimming and packaging, with **sequence-level quality ratings** to support filtering (e.g., “clean only” training vs. “wild” robustness training)
+
+## Diversity and covariate-shift robustness
+
+MicroAGI data is captured across **Europe and Asia**, intentionally spanning environments that create real-world distribution shift:
+
+* different homes, layouts, lighting regimes, materials
+* different hands/skins, tool choices, cultural cooking/object priors
+* varied camera motions and operator styles
+
+This is meant to be **covariate-shift resilient** data for models that need to generalize.
+
+## Optional derived signals (available on request)
+
+If you want more than raw RGB-D-IMU, MicroAGI can deliver *derived outputs* on top of the same sequences (or on newly captured data), such as:
+
+* **Ego-motion / trajectories** (VIO-style)
+* **SLAM reconstructions** (maps, trajectories, keyframes)
+* **Accurate body pose estimation**
+* **State-of-the-art 3D hand landmarks** (true 3D, not just 2D reprojections)
+* Additional QA layers and consistency checks tailored to your training setup
+
+These are provided as a service deliverable (and can be scoped to subsets / key frames / full coverage), depending on your needs.
 
 ## 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.
+* Each top-level sample folder typically contains:
+
+  * an MCAP “raw dump” folder
+  * an MCAP “processed/curated” folder (when applicable)
+  * an `mp4/` previews folder (when available)
+
+All authoritative signals are inside the **MCAP**. Use MP4s for fast browsing 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.
+* Python readers: `pip install mcap` (see the MCAP Python docs) or any MCAP-compatible tooling.
+
+Typical topics include RGB, depth, IMU, and any additional channels you may have requested.
 
 ## Intended uses
 
-* Policy and skill learning (robotics/VLA)
+* Policy and skill learning (robotics / VLA)
 * Action detection and segmentation
-* Hand/pose estimation and grasp analysis
+* Hand/pose estimation and grasp analysis (raw or with add-ons)
 * Depth-based reconstruction, SLAM, scene understanding
-* World-model pre-post training
+* World-model pre/post training
+* Robustness testing under real distribution shift
+
+## Data rights, consent, and licensing options
+
+All capture is **legally consented**, with **data rights documentation** attached. Depending on the engagement, rights can be structured as:
+
+* **non-exclusive** usage rights (typical dataset access), or
+* **exclusive** rights for specific task scopes / environments / cohorts (custom programs)
 
 ## 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
+* New data capture via our operator network (Europe + Asia)
+* ML-enhanced derived signals (ego-motion, pose, hands, SLAM)
+* Real-to-Sim pipelines and robotics-ready packaging
+* Custom QC gates to match your training/eval stack
 
 Typical lead times: under two weeks (up to four weeks for large jobs).
 
@@ -145,18 +127,20 @@ Email `data@micro-agi.com` with:
 
 * Task description
 * Desired hours or frame counts
+* Target environment constraints (if any)
+* Rights preference (exclusive / non-exclusive)
 * Proposed price
-  We will reply within one business day with lead time and final pricing.
+
+We 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)."
-
-
+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.
+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.