Update README.md

README.md

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 ---
-title: README
-emoji: 🚀
-colorFrom: red
-colorTo: pink
-sdk: static
-pinned: false
+license: other
+task_categories:
+  - robotics
+tags:
+  - robotics
+  - teleoperation
+  - lerobot
+  - vla
+  - world-model
+  - imitation-learning
+  - so-101
+pretty_name: DecisionFacts Physical AI Dataset
 ---
 
-Edit this `README.md` markdown file to author your organization card.
+# DecisionFacts Physical AI Dataset — SO-101 Robotic Arm Teleoperation
+
+## Data Summary
+
+This dataset is a curated collection of real-world **teleoperation data** captured on the **SO-101 robotic arm** (`so_follower`), built to support training and evaluation of modern robot-learning models — from imitation-learning policies to large-scale Vision-Language-Action (VLA) and world models.
+
+Each episode is a human-teleoperated demonstration of a manipulation task, recorded synchronously across two camera viewpoints alongside the arm's full proprioceptive state and control signals. Data collection followed a rigorous, standardized protocol:
+
+- **Expert teleoperation** — every episode is a deliberate, goal-directed demonstration performed by a trained operator, not scripted or simulated motion.
+- **Synchronized multi-modal capture** — joint states, actions, and dual-camera RGB video are captured in lock-step at 30 fps.
+- **Consistency checks** — episodes are reviewed for completeness, timing alignment, and successful task completion before being included in the released dataset.
+- **Structured, reproducible recording** — all data is captured and stored using the standardized [LeRobotDataset v3.0](https://huggingface.co/docs/lerobot/main/en/lerobot-dataset-v3) format, ensuring the dataset is immediately compatible with the broader open robot-learning ecosystem.
+
+This combination of careful human demonstration and disciplined data engineering is intended to make the dataset a dependable foundation for downstream policy and representation learning, rather than a loosely collected video corpus.
+
+## Dataset Structure
+
+The dataset follows the **Hugging Face LeRobotDataset v3.0** format. Unlike the earlier v2.1 format (one file per episode), v3.0 packs many episodes into a smaller number of larger, chunked files, with episode boundaries resolved through relational metadata rather than filenames. This makes the dataset scalable, faster to load, and streaming-ready directly from the Hub.
+
+```
+physical-ai/
+└── SO-101/
+    └── <task_name>/                 # e.g. cup_nesting
+        ├── data/
+        │   └── chunk-000/
+        │       └── file-000.parquet         # joint states, actions, indices — many episodes per file
+        ├── meta/
+        │   ├── info.json                    # schema, fps, robot type, chunking config
+        │   ├── stats.json                   # per-feature normalization statistics
+        │   ├── tasks.parquet                # task_index -> natural-language task description
+        │   └── episodes/
+        │       └── chunk-000/
+        │           └── file-000.parquet     # per-episode lengths, task refs, file/byte offsets
+        └── videos/
+            ├── observation.images.cam_front/
+            │   └── chunk-000/
+            │       └── file-000.mp4          # front-view camera, many episodes per file
+            └── observation.images.cam_top/
+                └── chunk-000/
+                    └── file-000.mp4          # top-down camera, many episodes per file
+```
+
+**Key structural points:**
+
+- **`data/`** — Apache Parquet shards containing frame-level `observation.state`, `action` (both 6-DoF: `shoulder_pan`, `shoulder_lift`, `elbow_flex`, `wrist_flex`, `wrist_roll`, `gripper`), plus `timestamp`, `frame_index`, `episode_index`, and `task_index`.
+- **`videos/`** — Two synchronized camera streams per episode:
+  - `observation.images.cam_front` — front-facing view of the workspace
+  - `observation.images.cam_top` — top-down view of the workspace
+  Both are AV1-encoded MP4 at 480×640, 30 fps, with no audio.
+- **`meta/`** — Self-describing metadata: schema/config (`info.json`), normalization stats (`stats.json`), the task-language mapping (`tasks.parquet`), and per-episode index records (`episodes/`).
+- **Episodes are not stored as individual folders or files.** Multiple episodes are concatenated into shared, size-capped chunk files (`chunk-000`, `chunk-001`, …). The exact location of any given episode — which chunk, which file, and its frame offset — is resolved by looking it up in `meta/episodes/`, not by filename. This is what allows the dataset to scale to many episodes and tasks without file-system overhead.
+- Each task (e.g. `cup_nesting`) is organized as its own self-contained LeRobotDataset directory under `SO-101/`, with its own `data/`, `meta/`, and `videos/` subfolders.
+
+## Methodology
+
+Data collection was designed to produce demonstrations that are diverse and robust enough for policies trained on them to generalize beyond the exact conditions seen during capture:
+
+- **Domain randomization** — object poses, positions, and scene conditions (e.g. object placement, orientation, and workspace configuration) were varied across episodes for each task, rather than repeating a single fixed setup. This exposes downstream models to a broader distribution of visual and spatial conditions during training, reducing overfitting to a narrow demonstration pattern.
+- **Episode volume** — a target of **30+ episodes per task** was used to ensure sufficient coverage of the randomized conditions and enough demonstration diversity for stable policy learning.
+- **Standardized capture pipeline** — every task follows the same recording protocol: 30 fps synchronized dual-camera capture, 6-DoF joint state/action logging, and consistent episode-level task-language annotation, so tasks are directly comparable and combinable during training.
+- **Human-in-the-loop quality control** — demonstrations are performed and reviewed by trained operators to ensure each episode reflects a coherent, successful execution of the intended task.
+
+## Intended Users
+
+This dataset is designed to support several stages of the physical-AI model development stack:
+
+- **VLA (Vision-Language-Action) training** — end-to-end training of policies that map visual observations and language instructions directly to robot actions.
+- **Post-training for robotics** — fine-tuning or adapting pretrained robot policies/foundation models to new manipulation tasks using targeted, high-quality demonstration data.
+- **VLM pre-training / post-training** — using the paired vision, language (task descriptions), and interaction data to improve grounding of vision-language models in physical, embodied contexts.
+- **World model training** — learning predictive models of environment and object dynamics from synchronized multi-view video and action/state sequences.
+
+Researchers and engineers working on imitation learning, robot foundation models, and embodied AI more broadly are the primary intended audience.
+
+## Citation
+
+If you use this dataset in your work, please cite it as follows:
+
+```bibtex
+@misc{decisionfacts_teleops_dataset,
+  credits      = {Prabhu Raghav, Sreeram B Uni, Balamurugan Pandi, Sriram Gopalan},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/datasets/DecisionFacts/physical-ai}}
+}
+```