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README.md

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+---
+dataset_info:
+  features:
+    - name: id
+      dtype: string
+    - name: game
+      dtype: string
+    - name: trial_id
+      dtype: int32
+    - name: episode_id
+      dtype: int32
+    - name: chunk_idx
+      dtype: int32
+    - name: frame_start
+      dtype: int32
+    - name: action
+      dtype: string
+    - name: action_ints
+      dtype: string
+    - name: score
+      dtype: int32
+    - name: reward_sum
+      dtype: int32
+    - name: gaze_positions
+      dtype: string
+    - name: image_bytes
+      dtype: binary
+license: mit
+task_categories:
+  - robotics
+  - reinforcement-learning
+tags:
+  - atari
+  - vla
+  - vision-language-action
+  - imitation-learning
+  - human-demonstrations
+  - action-chunking
+size_categories:
+  - 1M<n<10M
+---
+
+# TESS-Atari Stage 1 (15Hz)
+
+Human gameplay demonstrations from Atari games with **action chunking**, formatted for Vision-Language-Action (VLA) model training.
+
+## Overview
+
+| Metric | Value |
+|--------|-------|
+| Source | [Atari-HEAD](https://zenodo.org/records/3451402) |
+| Games | 11 (overlapping with DIAMOND benchmark) |
+| Samples | ~1.3M |
+| Observation Rate | 5 Hz |
+| Action Rate | 15 Hz (3 actions per observation) |
+| Format | Lumine-style action tokens |
+
+## Why Action Chunking?
+
+VLA models run at ~5 Hz inference speed, but Atari runs at 15 Hz (with frame_skip=4). Action chunking predicts 3 actions at once, matching the game's effective action rate while accommodating slower model inference.
+
+```
+Observation (5 Hz) → VLA → 3 Actions (executed at 15 Hz)
+```
+
+## Games Included
+
+Alien, Asterix, BankHeist, Breakout, DemonAttack, Freeway, Frostbite, Hero, MsPacman, RoadRunner, Seaquest
+
+## Action Format
+
+```
+<|action_start|> RIGHT ; RIGHT ; FIRE <|action_end|>
+<|action_start|> LEFT ; LEFT ; LEFT <|action_end|>
+<|action_start|> NOOP ; UP ; UPFIRE <|action_end|>
+```
+
+## Schema
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | string | Unique sample ID: `{game}_{trial}_{chunk}` |
+| `game` | string | Game name (lowercase) |
+| `trial_id` | int | Human player trial number |
+| `episode_id` | int | Episode within trial (-1 if unknown) |
+| `chunk_idx` | int | Chunk sequence number |
+| `frame_start` | int | First frame index of this chunk |
+| `action` | string | Lumine-style chunked action token |
+| `action_ints` | string | Raw ALE codes comma-separated: "4,4,1" |
+| `score` | int | Score at chunk start |
+| `reward_sum` | int | Total reward over 3 frames |
+| `gaze_positions` | string | Eye tracking from first frame |
+| `image_bytes` | bytes | PNG of first frame in chunk |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("TESS-Computer/atari-vla-stage1-15hz")
+
+# Get a sample
+sample = ds["train"][0]
+print(sample["action"])  # <|action_start|> RIGHT ; RIGHT ; FIRE <|action_end|>
+
+# Parse individual actions
+actions = sample["action_ints"].split(",")  # ["4", "4", "1"]
+
+# Decode image
+from PIL import Image
+from io import BytesIO
+img = Image.open(BytesIO(sample["image_bytes"]))
+```
+
+## Evaluation
+
+Use with [DIAMOND](https://diamond-wm.github.io/) world models (frame_skip=4). Execute the 3 predicted actions sequentially at each observation step.
+
+## Related
+
+- [5Hz variant](https://huggingface.co/datasets/TESS-Computer/atari-vla-stage1-5hz) - Single action per observation (simpler but slower)
+- [Lumine AI](https://www.lumine-ai.org/) - Inspiration for VLA architecture and action chunking
+- [DIAMOND](https://diamond-wm.github.io/) - World model for evaluation
+
+## Citation
+
+```bibtex
+@misc{atarihead2019,
+  title={Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset},
+  author={Zhang, Ruohan and others},
+  year={2019},
+  url={https://zenodo.org/records/3451402}
+}
+```