spiderpilot89/gg-physical-ground-truth

G^G Physical Ground Truth (Teaser Pack)

First-principles physical ground truth trajectory datasets for autonomous systems and Sim-to-Real calibration. Generated in pure Rust, sealed with SHA-256 hash chains, and verified on-chain.

Training robot brains in cartoon physics environments (like MuJoCo or NVIDIA Isaac Sim) leads to the Sim-to-Real Cliff. When your policy transitions from smooth simulators to physical hardware, it encounters non-deterministic slippage, thermal degradation, control loop jitter, sensor latency, and joint wear that linear models ignore.

G^G provides sovereign physical ground truth datasets modeling raw physics failures at 1000Hz (1ms step latency) to close the Sim-to-Real gap.

This teaser pack contains approx. 67,000 trajectories (approx. 3.4 GB total) across five critical domains.

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Quick Start

You can load any of the five configurations directly using the Hugging Face datasets library:

from datasets import load_dataset

# 1. Load Humanoid Centroidal & Joint Dynamics (50D)
humanoid = load_dataset("johnkruze/gg-physical-ground-truth", "humanoid", split="train")
print(humanoid[0])  # View first joint state step

# 2. Load Autonomous Vehicles & Heavy Logistics (51D)
vehicles = load_dataset("johnkruze/gg-physical-ground-truth", "vehicle", split="train")

# 3. Load Tactical Drones & Contested Airspace (59D)
drones = load_dataset("johnkruze/gg-physical-ground-truth", "drone", split="train")

# 4. Load Orbital Systems & Deep Space (20D)
orbital = load_dataset("johnkruze/gg-physical-ground-truth", "orbital", split="train")

# 5. Load Hypersonic Aerothermal Ablation (47D)
hypersonic = load_dataset("johnkruze/gg-physical-ground-truth", "hypersonic", split="train")

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Dataset Configurations

1. Humanoid Centroidal & Joint Dynamics — 50D

• Binary: humanoid_impedance_monte_carlo
• Volume: 15,000 trajectories (approx. 305 MB)
• Physics: Sagittal plane (xz) centroidal dynamics, pelvic pitch quaternion tracking, and Whole-Body Control (WBC) active joint-impedance. Simulates slip, recovery, and pelvic buckle/thermal failure on variable-stiffness terrain. Models gear backlash wear, ankle tendon elongation/creep, liquid payload slosh moments, and battery cell thermal runaway. Ground friction coefficient is swept ($\mu \in [0.10,0.60]$).

2. Autonomous Vehicles & Heavy Logistics — 51D

• Binary: vehicle_hydroplane_monte_carlo
• Volume: 10,000 trajectories (approx. 1.2 GB)
• Physics: 4-wheel chassis 6-DOF dynamics, wheel rotational states, dynamic normal loads, road moisture, and EKF estimator divergence signals. Tire interaction uses Pacejka Magic Formula force-slip equations ($F_x, F_y$ at all four corners). Models speed-dependent aquaplaning friction decay, hydraulic stiction during breakaway, and thermal damper fade (viscosity collapse above 120°C). Autopilot EKF is blind to lateral slip.

3. Tactical Drones & Contested Airspace — 59D

• Binary: drone_canopy_monte_carlo
• Volume: 20,000 trajectories (approx. 587 MB)
• Physics: Euler-Boussinesq wind-shear tensor coupling, ground cushion aerodynamics, dynamic dust obscuration, and motor thermal limits. Payload weights swept continuously (1.5 kg to 35.0 kg) with Parallel Axis Theorem inertia tensor updates during payload release (CoG migration). Active cyber-physical attacks: RF jamming (EW) inflating packet drop rates and control latency (up to 195ms), and adversarial GPS spoofing bias vectors triggering EKF divergence. Includes crystal oscillator clock drift (Bechmann polynomial).

4. Orbital Systems & Deep Space — 20D

• Binary: orbital_tumble_monte_carlo
• Volume: 12,000 trajectories (approx. 788 MB)
• Physics: Spacecraft attitude gyroscopic coupling integration (Euler equations) under fuel depletion, cross-inertia, solar storm torque, and fuel slosh dynamics. Models fuel slosh as a mass-spring-damper oscillator coupled to spacecraft attitude equations.

5. Hypersonic Aerothermal Ablation — 47D

• Binary: hgv_plasma_monte_carlo
• Volume: 10,000 trajectories (approx. 484 MB)
• Physics: Reentry 6-DOF trajectory propagation under Sutton-Graves convective heating, CoG migration from asymmetric ablation, nose cone shell thinning, wing flutter bend/twist, and EKF covariance trace tracking under plasma blackout GPS denial.

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Verification & Trust

Every trajectory in this dataset carries a SHA-256 proof_hash. The proof chain works as follows:

1. The G^G physics engine generates state vectors deterministically from seed parameters.
2. The final state vector is hashed: SHA-256(trajectory_data).
3. The hash is chained sequentially: SHA-256(prev_proof + current_proof).
4. The master proof chain anchor is stored immutably on the Internet Computer (ICP).

• Backend Canister: ad7wi-4aaaa-aaaad-aeijq-cai
• SOMA Ledger: mwtw4-wiaaa-aaaak-qx57a-cai (ICRC-1/2/3)

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Licensing & Commercial Access

This teaser pack is provided for evaluation and non-commercial academic research under the John Kruze Commercial License v1.

• Non-Commercial Use: You may download, inspect, and benchmark models using this teaser pack.

• Commercial Use: For commercial model training, production deployment, custom simulation requests, or buyout licensing for the 100,000-trajectory Sandbox datasets (totaling 26 GB), contact John Kruze directly:

  • Email: kruze@aijesusbro.com
  • Web: https://www.aijesusbro.com

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Citation

@dataset{kruze_gg_physical_ground_truth_2026,
  author = {Kruze, John},
  title = {G^G Physical Ground Truth: Trajectory Datasets for Embodied AI and Sim-to-Real Calibration},
  year = {2026},
  url = {https://huggingface.co/datasets/johnkruze/gg-physical-ground-truth},
  note = {First-principles Rust physics, SHA-256 proof chains, ICP verification}
}

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About John Kruze

John Kruze builds physics ground truth for autonomous systems. 13 substrates. First-principles Rust. SHA-256 sealed at every integration step. No game engines, no approximations.

The core thesis: autonomous systems operating without human-in-the-loop require deep physical reasoning, not visual game engine approximations. When GPS is denied, when communication is jammed, when the wind shear hits — your autopilot must reason from physics priors alone.

Website: www.aijesusbro.com
Author: John Kruze (LinkedIn)