| # 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. | |
| ## 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 | |
| * **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) | |
| * IMU data (as available) | |
| * **MP4 preview videos** (subset of sequences): | |
| * RGB preview (for quick visual QA) | |
| * Visualized depth preview (for quick visual QA) | |
| ## Labels / annotations https://www.youtube.com/watch?v=4-RVKBj2bcw | |
| 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 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 any additional channels you may have requested. | |
| ## Intended uses | |
| * Policy and skill learning (robotics / VLA) | |
| * Action detection and segmentation | |
| * Hand/pose estimation and grasp analysis (raw or with add-ons) | |
| * Depth-based reconstruction, SLAM, scene understanding | |
| * 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: | |
| * 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). | |
| ## How to order more | |
| 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 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. | |