Physical AI, taught and built.
The Bailey Military Institute division for the machines that see, think, and act: drones, robots, and autonomous systems. Education, research, and a living map of the field.
What we have actually made.
35 papers, 20 courses and 23 research topics, newest first. Drag the row, or use the arrows.
The Future of Commerce Using Physical AI
Embodied AI arrived in retail and a large share of it has already been taken out again. This review grades what stayed, what was withdrawn, and what both imply for the half-million people a year who enter the frontline commerce workforce.
The Energy-First Turn: Computing as a Function of Energy
Electricity, not transistors, is now what schedules the next model. This course teaches you to price computing in joules and to tell an honest energy
Quantum information at the edge
What quantum information science actually offers on-device Physical AI — mostly quantum-inspired structure, run classically, held with honest skeptici
The Energy Lab
Derive the memory tax yourself, then price the transition
RELAX/1
Ten operations, an MLIR and NIR-style exchange form, and a lowering matrix across substrates.
Physical AI for Space Logistics and Transportation
Space logistics is a velocity budget you compose and a chain of maneuvers an autonomy stack flies. Compute the rocket-equation wall and pay it down wi
Physical AI in space
Above the Kármán line there is no labor pool, so Physical AI is the space economy's labor supply rather than a substitute for it. The market, the unit
Physics-fidelity benchmark
Score whether a model obeys physics, not whether it looks right
OER/1 and DRIFT/1
A receipt schema for energy claims, and a benchmark protocol for learning under drift.
Energy First Architecture
The physics-first thesis, built in the browser: one scalar energy a body descends to act — and the same energy is the proof it will not diverge. Desce
Physical AI in the field
Telecom operators run one of the largest distributed physical estates in any industry and know its contents only approximately. What Physical AI can t
The live classroom
An AI teacher draws runnable simulations on a shared board
Honest Fluids: A Verified, Differentiable Computational Fluid Dynamics Stack for Physical AI, in the Browser
One open Rust stack spanning every solver paradigm — each checked against an analytic or reference oracle, differentiable end to end, and deployed self-verifying to WebAssembly.
Frontiers in Physical AI: Ternary
The multiply is the enemy. Three states {−1,0,+1} delete it — and with it the binary tax of multiplier arrays, the data-movement wall, and leading-edg
Building the energy compute future
A novel machine has to answer two questions at once: how it does on the work we already do, and what it does that the old machine cannot do at all.
Forge
Describe a robot, simulate it, then teach it to walk in your browser
One Digest: Bit-Identical Neural Computation Across Heterogeneous Compute Fabrics
Making a transformer forward pass compute the same bits in a browser tab, on Apple silicon, and on an NVIDIA GPU — the mechanisms that break it, the pins that hold it, and the measured price.
Physics-Informed Physical AI
The physical world will not be solved by data alone: action-labeled data is scarce, and a black-box net that has never heard of energy or momentum ext
The adaptive unit
Learning without forgetting — the dendrite as a small network.
The global energy database
Eighty-five claims, each with its verification grade
Perception to Policy: From a Sensor to a VLA
The ladder from a raw sensor to a vision-language-action policy, one rung at a time, with a model you train on-device at every step: turn a sensor str
The Diffusion Layer: Accelerating the Spread of Silicon-Design Competency for Physical AI — A Survey and Research Position
Physical AI is bottlenecked less by fabs than by the people who can design for them. This report treats that competency as an innovation to diffuse, and the training as the technology that sets its speed.
The energy-first turn
Electricity, not transistors, is now the binding constraint on computing. What the escape looks like, who is building it, and what would count as proo
Pilot
Drive an embodied policy with a real controller
The Human Layer: Competency at the Frontier of Autonomous Systems — A Survey and Research Position
As machines take the loop, the human role does not disappear; it moves to the boundary. This report maps the competency that lives there, why automation erodes it, and how to build it deliberately.
Agentic Physical AI: The Open Skill Layer
Build the open, cross-vendor skill layer that turns any policy into a robot skill: it installs, checks the body's declared capabilities against what t
Touching sound
Contact audio: the sense that works through occlusion and in the dark, and reveals what light and touch can't.
The Bayou Air Corridor: Managed Low-Altitude Airspace over Houston's Greenways
Treating a continuous public greenway network as an AI-managed low-altitude volume, with flood search-and-rescue as the first mission and infrastructure inspection as the recurring one.
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Every physical machine runs one loop.
Perceive, simulate, act, then again, faster than thought. It's the single idea beneath every course, lab, and system here.
It sees.
Cameras, LiDAR, and touch collapse into one picture of the world: depth, objects, and the free space to move through, built on the device in milliseconds.
It predicts.
Before it moves, the machine runs the future in a digital twin, many routes and many rollouts, and keeps the plan that survives contact with reality.
It acts.
Before it commits, the action is checked against the one energy the body descends — a proof it won't diverge — then becomes motion: torque, balance, grip. The world it's moving through changes.
It never stops.
Perceive, simulate, act, then perceive again, many times a second. That closed loop — on the device, certifying each action before it commits — is agency: a body that holds its own boundary, on its own power. Intelligence is the other axis.
Six doors in.
Learn it, build it, play it, discover it, map it, launch it. Start wherever you are.
Education
Instructors, courses, and live labs.
Open →
The Lab
Every runnable bench in one place — write code, drive robots, run the physics.
Open →
Play
The Arena and the games — code your way onto the leaderboard.
Open →Research
Researchers, projects, and reproducible experiments.
Open →Intelligence
A living, geographic knowledge map of Physical AI.
Open →
Entrepreneurship
Start a company, get startup-ready, pitch, and get funded.
Open →Looking for something specific? Search all 168 instruments, courses, papers & topics ⌕ →
The software behind the lessons.
Physics simulation, CAD, and compiled firmware: the engines each course runs on. Open one to try it.
Robot control
Write a control loop and a real differential-drive robot drives to the goal on DeepMind's MuJoCo physics.
MuJoCo · PythonTry it live →Visual programming
Snap blocks together. They become real Python that drives the same robot.
Blockly → PythonTry it live →
Parametric CAD
Model a part in code on the actual OpenCascade kernel engineers use, and watch the solid render.
build123d · OpenCascadeTry it live →
Embedded Rust
Write no_std firmware; it compiles in the cloud to WebAssembly and runs as the robot's real control loop.
Rust → WASM · edge-compiledTry it live →Device physics
Tune a mechanism's parameters and watch real physics settle it to spec.
MuJoCo · MJCFTry it live →Factory twin
Balance a virtual production line (bottlenecks, throughput, cost) as a live simulation.
Discrete-event simTry it live →Train, research, and partner with us.
The Institute for Physical AI is an autonomous division of Bailey Military Institute, a 501(c)(3) aviation-education nonprofit. Explore Bailey, its courses, and how to support the mission.