Datasets:

TESS-Computer
/

atari-vla-stage1-15hz

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
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.

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}
}