Physical AI
Infrastructure
at Scale

# SN-12S :: Physics Simulation Pipeline
import numpy as np
from sn12s.sim import RobotEnv, DomainRandomiser
from sn12s.train import FoundationPolicy, TrajectoryBuffer
from sn12s.deploy import EdgeRuntime, SafetyMonitor

# Initialise simulation environment with domain randomisation
env = RobotEnv(
    robot_model="sn12s_humanoid_v2",
    physics_engine="omniverse_newton",
    num_envs=4096,           # parallel simulation instances
    gravity_randomise=True,  # for space/lunar variants
    render_mode="headless",
)

randomiser = DomainRandomiser(
    friction_range=[0.1, 0.9],
    mass_scale=[0.8, 1.2],
    latency_ms=[0, 12],
)

# Load foundation model checkpoint (GR00T-based)
policy = FoundationPolicy.from_checkpoint(
    path="/mnt/models/sn12s_gr00t_n1.6_finetuned.pt",
    device="cuda:0",
    dtype=np.float16,
)

# Rollout loop — 1M steps before real deployment
buffer = TrajectoryBuffer(capacity=1_000_000)
for step in range(1_000_000):
    obs = env.reset_if_done()
    obs = randomiser.apply(obs)
    action = policy.infer(obs)                # <10ms
    obs_next, reward, done, info = env.step(action)
    buffer.push(obs, action, reward, obs_next, done)
    if step % 10_000 == 0:
        policy.update(buffer.sample(2048))

# Deploy to edge with safety monitor
runtime = EdgeRuntime(target="jetson_agx_thor")
monitor = SafetyMonitor(standard="ISO26262", asil="D")
runtime.deploy(policy, safety_monitor=monitor, ota=True)

// SN-12S Simulation Engine — Newton Physics Core (C++)
#include "sn12s/physics/rigid_body.hpp"
#include "sn12s/physics/soft_tissue.hpp"
#include "sn12s/usd/scene_manager.hpp"
#include "sn12s/data/synthetic_pipeline.hpp"

// 4096 parallel sim instances on single OVX node
constexpr int NUM_ENVS = 4096;
constexpr float DT     = 0.002f;  // 500Hz physics step

SN12SSimulation::SN12SSimulation() {
    scene_ = SceneManager::from_usd(
        "omniverse://localhost/Scenes/jurong_island_v3.usd"
    );
    physics_ = std::make_unique<RigidBodySolver>(
        SolverConfig{ .num_envs=NUM_ENVS, .dt=DT,
                     .enable_ccd=true, .gravity={0,-9.81f,0}}
    );
    soft_tissue_ = std::make_unique<SoftBodyFEM>(
        FEMConfig{ .material="hepatic_parenchyma",
                  .youngs_modulus=3000.0f,  // Pa
                  .poisson_ratio=0.46f}
    );
}

void SN12SSimulation::step_all() {
    // CUDA kernel dispatched across NUM_ENVS
    physics_->simulate_parallel(DT);
    soft_tissue_->integrate_fem(DT);
    synthetic_pipeline_->generate_sensor_data();
}

// SN-12S Edge Runtime — ARM64 / Jetson AGX Thor
#include "sn12s/runtime/inference_engine.hpp"
#include "sn12s/safety/iso26262_monitor.hpp"
#include "sn12s/comms/ros2_bridge.hpp"

class SN12SRuntime {
public:
    SN12SRuntime(const DeployConfig& cfg) {
        engine_ = TensorRTEngine::load(cfg.model_path,
            { .precision="FP16", .dla_core=0});
        monitor_ = std::make_unique<ISO26262Monitor>(
            ASILLevel::D);
        ros_bridge_ = std::make_unique<ROS2Bridge>(
            "sn12s_inference_node");
    }

    // Real-time loop — must meet <10ms deadline
    void run_rt_loop() {
        while (running()) {
            auto obs = ros_bridge_->get_observation();
            monitor_->validate_input(obs);
            auto t0 = Clock::now();
            auto action = engine_->infer(obs);
            auto latency = Clock::since(t0);
            if (latency > 10ms) monitor_->alert();
            monitor_->validate_action(action);
            ros_bridge_->publish_action(action);
        }
    }
private:
    std::unique_ptr<TensorRTEngine>  engine_;
    std::unique_ptr<ISO26262Monitor> monitor_;
    std::unique_ptr<ROS2Bridge>      ros_bridge_;
};