TESS-Computer/atari-vla-stage1-15hz

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
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

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 world models (frame_skip=4). Execute the 3 predicted actions sequentially at each observation step.

Related

• 5Hz variant - Single action per observation (simpler but slower)
• Lumine AI - Inspiration for VLA architecture and action chunking
• DIAMOND - World model for evaluation

Citation

@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}
}