Datasets:

TESS-Computer
/

atari-vla-stage1-5hz

dataset_info:
  features:
    - name: id
      dtype: string
    - name: game
      dtype: string
    - name: trial_id
      dtype: int32
    - name: episode_id
      dtype: int32
    - name: frame_idx
      dtype: int32
    - name: action
      dtype: string
    - name: action_int
      dtype: int32
    - name: score
      dtype: int32
    - name: reward
      dtype: int32
    - name: reaction_time_ms
      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
size_categories:
  - 1M<n<10M

TESS-Atari Stage 1 (5Hz)

Human gameplay demonstrations from Atari games, formatted for Vision-Language-Action (VLA) model training.

Overview

Metric	Value
Source	Atari-HEAD
Games	11 (overlapping with DIAMOND benchmark)
Samples	~4M
Action Rate	5 Hz (1 action per observation)
Format	Lumine-style action tokens

Games Included

Alien, Asterix, BankHeist, Breakout, DemonAttack, Freeway, Frostbite, Hero, MsPacman, RoadRunner, Seaquest

Action Format

<|action_start|> FIRE <|action_end|>
<|action_start|> LEFT <|action_end|>
<|action_start|> RIGHTFIRE <|action_end|>

Schema

Field	Type	Description
`id`	string	Unique sample ID: `{game}_{trial}_{frame}`
`game`	string	Game name (lowercase)
`trial_id`	int	Human player trial number
`episode_id`	int	Episode within trial (-1 if unknown)
`frame_idx`	int	Frame sequence number
`action`	string	Lumine-style action token
`action_int`	int	Raw ALE action code (0-17)
`score`	int	Current game score
`reward`	int	Immediate reward
`reaction_time_ms`	int	Human decision time in ms
`gaze_positions`	string	Eye tracking data (x,y pairs)
`image_bytes`	bytes	PNG image of game frame

Usage

from datasets import load_dataset

ds = load_dataset("TESS-Computer/atari-vla-stage1-5hz")

# Get a sample
sample = ds["train"][0]
print(sample["action"])  # <|action_start|> FIRE <|action_end|>

# Decode image
from PIL import Image
from io import BytesIO
img = Image.open(BytesIO(sample["image_bytes"]))

Evaluation

Designed for evaluation in DIAMOND world models on the Atari 100k benchmark.

15Hz variant - 3 actions per observation for faster gameplay
Lumine AI - Inspiration for VLA architecture
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}
}