Haga - Independent Physical AI Verification Platform & Dual-Surface Web Architecture
As Physical AI (robotics, autonomous systems) rapidly transitions to training via synthetic data and simulated world models, the industry faces a crisis of trust: labs self-report benchmarks, and synthetic environments often suffer from undetected physics inconsistencies. Haga is a complete, end-to-end Physical AI verification platform comprising Haga Core (a Python-based verification layer that stress-tests robot learning policies in MuJoCo) and Haga Web (a secure PNPM monorepo with dual Next.js surfaces for public research marketing and private investor diligence). Together, this architecture delivers independent, third-party validation for Physical AI startups while airgapping source code and gating sensitive telemetry.
As Physical AI (robotics, autonomous systems) rapidly transitions to training via synthetic data and simulated world models, the industry faces a crisis of trust: labs self-report benchmarks, and synthetic environments often suffer from undetected physics inconsistencies. Haga is a complete, end-to-end Physical AI verification platform comprising Haga Core (a Python-based verification layer that stress-tests robot learning policies in MuJoCo) and Haga Web (a secure PNPM monorepo with dual Next.js surfaces for public research marketing and private investor diligence). Together, this architecture delivers independent, third-party validation for Physical AI startups while airgapping source code and gating sensitive telemetry.
The Challenge
Evaluating Physical AI policies and presenting benchmarking evidence presents a dual technical hurdle:
- Simulation & Physics Verification: Robot learning labs lack third-party tools to detect object permanence failures, impossible accelerations, or mass/friction policy thresholds.
- Dual-Surface Trust Architecture: Haga needed a web infrastructure to publicly publish sanitized benchmark metrics for researchers while maintaining a cryptographically gated, invite-only dataroom for investors without risking IP exposure or data drift between public claims and private financials.
The Solution: Core Physics Engine & Dual-Surface Monorepo
Core Technical Pillars:
- Deterministic Physics Benchmarking (Haga Core): Interfacing directly with
mujocoandrobosuite, the Python benchmark runner systematically perturbs environmental variables (mass, friction, damping) across thousands of episodes to calculate policy failure thresholds. - Physics-Consistency Scoring (PhysicsIQ): Utilizing OpenCV and PyTorch (
cotracker), Haga Core analyzes AI-generated video frames to detect spatial anomalies and non-Newtonian movement. - PNPM Workspaces & Zero-Drift Data Architecture: Haga Web is structured as a monorepo with shared packages (
@haga/brandand@haga/metrics). Both the public site and private dataroom consume identical sanitized JSON telemetry from Haga Core via GitHub Releases. - Gated Dataroom Security:
apps/dataroomutilizes Auth.js allowlisting and cryptographic business logic (incorporation.ts) to release sensitive operational data to investors based on legal funding milestones.
Key Features & Business Impact
1. Verification-as-a-Service
Provides actionable "Pass/Fail/Score" metrics for robot learning policies before deployment to multi-million-dollar hardware setups.
2. Airgapped Public/Private Surfaces
Allows researchers to view live benchmarking metrics on the public site, while enabling founders to run confidential investor diligence in an MDX-powered dataroom without direct database access to the core physics engine.
Empirical Evidence & Outcomes
- Reproducibility: Strict Python dependency management (
pyproject.toml) and automated GitHub Actions CI guarantee benchmark parity across local and cloud environments. - Zero-Drift Guarantee: Both public site and private dataroom import the exact same
@haga/metricspackage, guaranteeing public claims match private investor data.