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--- |
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license: mit |
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task_categories: |
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- robotics |
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- image-to-text |
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tags: |
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- VLA |
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- gaming |
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- counter-strike |
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- behavioral-cloning |
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- imitation-learning |
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- action-chunking |
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size_categories: |
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- 1M<n<10M |
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--- |
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# CS:GO VLA Stage 1 Dataset (5Hz Chunked) |
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Vision-Language-Action dataset for Counter-Strike: Global Offensive with action chunking, converted from the [TeaPearce CS:GO dataset](https://huggingface.co/datasets/TeaPearce/CounterStrike_Deathmatch). |
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## Overview |
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- **Frame rate:** 5Hz (every 3rd frame) |
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- **Action chunking:** 3 actions per sample (~200ms coverage) |
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- **Total samples:** ~1.8M chunks |
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- **Split:** train / test following [Diamond](https://github.com/eloialonso/diamond) split |
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- **Map:** Dust2 deathmatch |
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## Action Format |
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``` |
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<|action_start|> m1_x m1_y [keys1] ; m2_x m2_y [keys2] ; m3_x m3_y [keys3] <|action_end|> |
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``` |
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**Examples:** |
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``` |
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<|action_start|> 0 0 ; 0 0 ; 0 0 <|action_end|> # idle |
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<|action_start|> 5 0 W ; 3 0 W ; 4 0 W <|action_end|> # walking |
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<|action_start|> -200 50 W L ; -50 10 L ; 10 0 W <|action_end|> # flick shot |
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``` |
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Each chunk contains the exact mouse delta and keys for that frame - no aggregation. |
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## Schema |
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| Column | Type | Description | |
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|--------|------|-------------| |
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| `id` | string | Unique sample ID | |
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| `episode_id` | string | Source HDF5 file | |
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| `chunk_idx` | int32 | Chunk number within episode | |
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| `frame_idx` | int32 | Starting frame number | |
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| `action` | string | Text-formatted 3-action chunk | |
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| `kill_flag` | int32 | 1 if any kill in chunk | |
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| `death_flag` | int32 | 1 if any death in chunk | |
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| `split` | string | "train" or "test" | |
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| `image_bytes` | bytes | JPEG screenshot (first frame) | |
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## Usage |
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```python |
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from datasets import load_dataset |
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# Load full dataset |
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ds = load_dataset("TESS-Computer/csgo-vla-stage1-5hz") |
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# Filter by split |
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train_ds = ds.filter(lambda x: x['split'] == 'train') |
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test_ds = ds.filter(lambda x: x['split'] == 'test') |
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``` |
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## Why 5Hz with Chunking? |
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1. **VLA inference speed:** 62ms (16Hz) is too fast for current VLMs. 200ms (5Hz) is achievable. |
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2. **No information loss:** Each chunk predicts exactly what the human did for 3 consecutive frames. |
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3. **World model sync:** Diamond executes `step(a1), step(a2), step(a3)` then returns frame to VLA. |
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## Related |
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- [16Hz variant](https://huggingface.co/datasets/TESS-Computer/csgo-vla-stage1-16hz) - 1 action per frame |
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- [Diamond World Model](https://github.com/eloialonso/diamond) - For evaluation |
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- [Original Dataset](https://huggingface.co/datasets/TeaPearce/CounterStrike_Deathmatch) |
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