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README.md

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-title: README
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+title: EXYLOS
+emoji: 🤖
+colorFrom: blue
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-Edit this `README.md` markdown file to author your organization card.
+<p align="center">
+  <h1 align="center">EXYLOS</h1>
+  <p align="center"><strong>Robot-ready skill datasets for manipulation policy learning and evaluation.</strong></p>
+</p>
+
+---
+
+## What we do
+
+EXYLOS builds structured robot manipulation datasets for imitation learning, VLA models, policy training, and evaluation.
+
+Raw videos show what happened, but policy learning also needs synchronized actions, states, task metadata, outcomes, and failure context. EXYLOS turns human-seeded manipulation workflows performed in our simulations into train-ready episodes with multi-view observations, trajectories, annotations, success/failure labels, and quality diagnostics.
+
+---
+
+## Public sample datasets
+
+This organization hosts compact inspection samples for checking schema, loading data, inspecting trajectories, and evaluating whether the EXYLOS format fits a robotics ML stack.
+
+| Dataset | Status | Contents |
+|---|---:|---|
+| [`ExylosAi/pick_and_place_sample`](https://huggingface.co/datasets/ExylosAi/pick_and_place_sample) | Available | 50 pick-and-place episodes, 21,412 frames, 5 RGB views, 9D Panda state/action, 30 success and 20 failure episodes. |
+| [`ExylosAi/bimanual_table_spill_cleanup`](https://huggingface.co/datasets/ExylosAi/table_spill_cleanup_bimanual) | Available | 50 bimanual spill-cleanup episodes, 67,742 frames, 6 RGB views, 18D dual-Panda state/action, 35 success and 15 failure episodes. |
+
+More contact-rich and recovery-heavy samples are planned.
+
+---
+
+## Dataset format
+
+EXYLOS samples are packaged to be compatible with the LeRobot ecosystem whenever possible. A typical dataset contains:
+
+```text
+README.md
+LICENSE.txt
+annotations.json
+meta/
+  info.json
+  tasks.jsonl
+  episodes.jsonl
+  episodes_stats.jsonl
+data/
+  chunk-000/
+    episode_000000.parquet
+    episode_000001.parquet
+videos/
+  chunk-000/
+    observation.images.<camera_name>/
+      episode_000000.mp4
+      episode_000001.mp4
+```
+
+Core signals:
+
+| Category | Examples |
+|---|---|
+| Visual observations | Synchronized RGB wrist and scene views |
+| Action and state | Robot state, action vectors, timestamps, frame indices |
+| Labels | Success, failure reason, terminal flags, collisions, aborts, retries |
+| Annotations | Phase boundaries, hand labels, object notes, scores, derived metrics |
+| Metadata | Task description, duration, splits, feature schema, validation stats |
+
+Exact fields vary by dataset, so each repository includes a dataset-specific card.
+
+---
+
+## Quick start
+
+Install LeRobot and load one of the public samples:
+
+```bash
+pip install lerobot
+```
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+dataset = LeRobotDataset("ExylosAi/pick_and_place_sample")
+
+sample = dataset[0]
+print(sample.keys())
+print(sample["observation.state"].shape)
+print(sample["action"].shape)
+```
+
+Raw Parquet, MP4, and JSON files are also available in each dataset repository.
+
+---
+
+## Why EXYLOS datasets are different
+
+- **Structured, not raw:** episodes include synchronized video, actions, state, metadata, annotations, and quality checks.
+- **Skill-oriented:** each dataset is organized around a manipulation workflow rather than unrelated clips.
+- **Failure-aware:** samples include failed attempts, aborts, collisions, incomplete task executions, and recovery-relevant labels when available.
+- **LeRobot-oriented:** data is stored in open formats such as MP4, Parquet, JSON, and JSONL.
+- **Transfer-minded:** workflows are captured from human intent in consumer VR and procedurally expanded, with added visual domain randomization for broader policy-learning experiments.
+
+---
+
+## Intended use
+
+Public samples are suitable for:
+
+- inspecting EXYLOS schema and annotation conventions
+- testing LeRobot-compatible loaders and training pipelines
+- running small imitation-learning experiments
+- reviewing multi-view video, trajectories, and phase annotations
+- evaluating whether a custom EXYLOS skill pack would fit your workflow
+
+They are compact inspection datasets, not complete production-scale benchmarks.
+
+---
+
+## Commercial datasets and custom skill packs
+
+EXYLOS can generate custom robot-ready skill datasets for pick-and-place, bimanual manipulation, spill cleanup, sorting, binning, object rearrangement, failure recovery, and evaluation/regression sets.
+
+Commercial deliveries can add depth, segmentation masks, object states, event labels, custom cameras, larger episode volumes, stricter QA, and internal-pipeline packaging.
+
+---
+
+## License
+
+Current public samples are released under Apache 2.0. Please check each dataset card and license file before using a sample in research, demos, training, or commercial workflows.
+
+---
+
+## Contact
+
+- Website: [exylos.ai](https://exylos.ai)
+- Email: contact@exylos.ai
+- LinkedIn: [Exylos on LinkedIn](https://www.linkedin.com/company/exylos-ai/)
+
+If you need structured skill data, send us the target task, robot, modalities, format, evaluation criteria, and timeline.