Datasets:

sarahnator
/

genesis-physical-interventions

license: mit
task_categories:
  - visual-question-answering
  - video-classification
  - robotics
language:
  - en
tags:
  - physics
  - physical-reasoning
  - world-models
  - embodied-ai
  - genesis
  - simulation
  - counterfactual
pretty_name: Genesis Physical Intervention Benchmark

Genesis Physical Intervention Benchmark

This dataset contains controlled Genesis simulations for evaluating physically viable world models in embodied AI settings.

Repository: sarahnator/genesis-physical-interventions Visibility at upload time: private

Scenes

Ramp-cup-water
Robotic pour
Pendulum

Task Types

Single-rollout outcome prediction
Scalar physical prediction
Inverse parameter prediction
Pairwise counterfactual comparison

Files

data/vlm_entries.jsonl
data/manifest_all.jsonl
scenes/
artifacts/frames/
artifacts/videos/
artifacts/trajectories/

Dataset Size Summary

Number of VLM entries: 111

Entries by scene:

{
  "pendulum": 38,
  "ramp_cup": 31,
  "robotic_pour": 42
}

Entries by query type:

{
  "binary_outcome": 53,
  "inverse_parameter_prediction": 12,
  "pairwise_counterfactual": 15,
  "scalar_prediction": 31
}

Loading Example

from datasets import load_dataset
ds = load_dataset("sarahnator/genesis-physical-interventions", data_files="data/vlm_entries.jsonl", split="train")
print(ds[0])

Limitations

Labels are simulator-derived, not real-world measurements.
Some labels use proxies, such as receiver_fraction for robotic pouring.
This is a research benchmark for physical reasoning, not a complete real-world robotics benchmark.