Physical AI Development Platform

Build robot intelligence
through simulation.

Write custom kernels for robotics models, discover reward functions with LLM-guided training, and run GPU-accelerated simulations—with reproducible experiments from design to deployment.

GPU-accelerated simulationCustom CUDA kernelsReward function discoverySim-to-real transferReproducible experiments

VIEW DOCS See the platform ↓

Integrates with Claude Code and any MCP-compatible development environment.

Isaac Sim + Isaac LabCUDA kernel developmentLLM-guided reward functions3D reconstruction pipelineReproducible experiment traces

How it works

From design to deployment in one workflow.

Define your robot and task, run physics-accurate simulation, train policies with LLM-guided reward discovery, and produce deployment-ready artifacts—all tracked and reproducible.

Design

Robot morphology + task spec

Simulate

GPU-accelerated physics

Train

RL + LLM reward discovery

Deploy

Validated models + artifacts

Platform

One workflow from specification to trained policy.

Define your robot task, run GPU-accelerated simulation, iterate on reward functions with LLM guidance, and export deployment-ready artifacts. Every step is traced and reproducible.

Specification

Define robot task, constraints, reward structure, and simulation parameters.

Simulation

isaac.create_physics_scene()
isaac.create_robot(
  robot_type="g1"
)
isaac.configure_task(
  reward_spec="reach_target"
)

Training

eureka.discover_reward()  ✓
train.iteration(1..500)   ✓
eval.convergence_check    ✓
reward.evolve(gen=3)      ✓
train.iteration(1..1000)  ✓
→ policy: trained_v3.pt

Artifacts

[+]trained_policy.pt
[+]reward_function.py
[+]training_trace.jsonl
[+]simulation.usdz

Traces are inspectable. Artifacts are versioned. Experiments are reproducible.

What you can do today

Simulate, train, deploy.

Robotics Simulation

Run GPU-accelerated robotics simulations

Compose scenes, configure physics, and run multi-robot environments with Isaac Sim and Isaac Lab.

isaac.create_physics_scene()
isaac.create_robot(robot_type="g1")
isaac.run_simulation(steps=10000)

Robot Learning

Train policies with LLM-guided reward discovery

Discover reward functions automatically with Eureka, train RL policies, and iterate on robot behaviors.

eureka.create_run(
  task="locomotion",
  robot="g1",
  iterations=5
)

Kernel Development

Write and deploy custom CUDA kernels

Develop custom physics kernels, optimize inference pipelines, and produce deployment-ready binaries.

kernels.compile(src="contact_model.cu")
kernels.benchmark(steps=1000)
→ speedup: 3.2x

Capabilities

Everything you need to build robot intelligence.

Developer API

Programmatic access to simulation, training, and deployment workflows. Integrate with Claude Code or any MCP-compatible client.

Simulation Engine

GPU-accelerated robotics simulation powered by Isaac Sim and Isaac Lab. Digital twin creation, synthetic data generation, and physics-accurate environments.

3D Reconstruction

Photogrammetry to USDZ pipeline. Capture real-world environments and generate simulation-ready digital twins.

Experiment Management

Reproducible experiment tracking, artifact versioning, and team collaboration for robotics research.

Get Started

Start building with research credits.

Researcher

For individual researchers

Everything you need to start running experiments.

[+]Simulation environments
[+]Experiment tracking + traces
[+]Artifact storage + versioning
[+]API + MCP access

Team

Research collaboration

For research teams that need shared environments and controls.

[+]Team workspaces
[+]Shared experiments + access controls
[+]Priority compute allocation
[+]Dedicated support

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Research credits scale with your project. Budget controls and idle shutdown included.

Connect

Connect your development environment

Add this to your MCP client config. Works with Claude Code and any MCP-compatible IDE. Scoped keys recommended.

MCP Server Config

{
  "mcpServers": {
    "cyberneticphysics": {
      "transport": "http",
      "url": "https://api.cyberneticphysics.com/mcp",
      "headers": {
        "Authorization": "Bearer <cp_live_...>"
      }
    }
  }
}

FAQ

Common questions

What simulation environments are supported?

Isaac Sim, Isaac Lab, and custom URDF/USD environments. You can import your own robot models or use built-in platforms like G1, H1, and standard manipulators.

How does LLM-guided reward discovery work?

Our Eureka integration uses large language models to generate and iterate on reward functions automatically. The LLM proposes reward code, trains a policy, evaluates performance, and evolves the reward—producing better behaviors than hand-tuned rewards.

Can I write custom CUDA kernels?

Yes. The platform supports custom kernel development for physics models, contact dynamics, and optimized inference pipelines. Compile, benchmark, and deploy directly from your workspace.

How do experiments stay reproducible?

Every run produces a full trace: simulation parameters, reward functions, training curves, and versioned artifacts. Rerun any experiment from its trace to get identical results.

What robot platforms are supported?

Humanoids, manipulators, mobile robots, and quadrupeds. Any robot with a URDF or USD description can be imported into the simulation environment.

Ready to accelerate
your robotics research?

Start running GPU-accelerated simulations, discover reward functions, and produce deployment-ready robot policies.

VIEW DOCS

Build robot intelligencethrough simulation.