Machines that sense, decide, and act.
Humanoid robots, autonomous vehicles, drones, industrial robotics, and intelligent medical systems. Every one of them is a software-defined machine.
Core questionWhat should the machine do?
AI Physics × Physical AI
Physical AI needs AI Physics.
Intelligence decides what a machine should do. Execution reality determines whether its software can keep up.
LOCI gives AI coding agents execution awareness for robotics software — before the code runs.
What is AI PhysicsWhat is AI Physics01 / Physical AI 02 / AI Physics
Two ideas. One physical world.
AI grounded in real constraints.
Models of the computational execution behind every decision — timing, energy, memory — that tell you whether the behavior is feasible on the machine that has to run it.
Core questionCan it operate within its constraints?
AI Physics predicts, from the compiled binary
TimingLatencyPowerEnergyMemoryCacheRegressions
Why Physical AI needs it
A smart robot can still miss a deadline.
The algorithm may be correct. But CPU demand, memory pressure, interrupts, and execution paths change how the machine behaves.
Mechanical physics matters. So does the execution behavior of the software running on its processors.
Sim-to-real
Simulation proves the robot. It does not prove the code.
A controller that reaches the dock in simulation can still miss its window on the Orin it ships on. The physics gap is closed. The execution gap is not.
Simulation answers
Does the robot do the task?
- Physics, contact and dynamics
- Perception and navigation convergence
- Scenario coverage in a virtual world
LOCI answers
Will the code hold on the target?
- Control-loop worst case against its deadline
- Power and memory inside their envelopes
- Behaviour when perception contends for the same silicon
Complementary, not competing. Simulation stays the authority on whether the task succeeds — LOCI reads the compiled binary and predicts whether the code behind it holds on Jetson, Orin and the rest of the target list.
The Picture·Physical AI
AI Physics meets Physical AI.
A robot hand learns from a camera and pressure sensors. Claude Code writes its controllers and motion intelligence — and LOCI grounds that software on the robot’s real silicon, so a confident policy never becomes a motion the hardware can’t safely execute.
Camera + pressure trained
Claude writes the controllers
LOCI predicts the physics
Grounded, not hallucinated
Real siliconNo hallucinated motionPhysical AI
Robot hand · Physical AI real silicon
CameravisionPressuretouchLOCIpre-run
AI-written controllers, grounded on the robot’s real silicon before they move a motor.
Live·Physical AI
Watch LOCI catch the hallucination.
Claude Code writes the hand’s grasp controller — confident it’s safe. LOCI predicts how it actually runs on the MCU and stops the motion that would overrun the control period, before it ever drives a servo.
The agent fixes its own code — before a single line moves the hand.
robot-hand · grasp_controller.c + LOCI MCP live
In your loop
Execution physics for AI coding agents.
LOCI predicts how compiled software will behave on the CPUs and GPUs robots ship on — without running or simulating it.
- 01
Agent writes code
Claude or another coding agent implements a robotics feature.
- 02
Build
The software is compiled for the target — Jetson, Orin, or the microcontroller it ships on.
- 03
LOCI evaluates
Predicted timing, workload, memory and execution risks are checked against the budgets the system must live inside.
- 04
Agent improves
The agent revises with that evidence, so simulation and hardware confirm instead of discover.