AI Agent

Meet Optimus

An autonomous AI agent that writes code, analyzes robot models, controls simulations, and iterates on training — without human intervention.

Architecture

The autonomous training loop

Optimus operates in a continuous cycle — observing, hypothesizing, acting, judging, and deciding — until the policy converges.

SEEObserve simulation stateHYPOTHESIZEForm a reward hypothesisACTExecute training runJUDGEEvaluate policy performanceDECIDEIterate or convergeAutonomousLoop

Capabilities

What Optimus can do

Deep robotics domain knowledge paired with autonomous coding and simulation control.

Code Generation

Writes reward functions, training configs, and environment wrappers from task descriptions.

Model Analysis

Inspects joint trees, actuator properties, kinematic chains, and collision geometry.

Simulation Control

Loads models, runs physics, queries state, and visualizes sensor data in real-time.

Training Iteration

Launches training runs, monitors convergence, evaluates policies, and decides next steps.

Results

Before and after

Seven autonomous iterations. Zero human intervention. From random actions to a converged walking policy.

Before Training

Untrained policy — random actions

After Training

Trained policy — 94% success rate

In action

Watch Optimus work

Give a task in plain language. Optimus handles model analysis, reward design, training dispatch, and convergence — autonomously.

optimusagent
[user]

Let Optimus handle the iteration

Stop hand-tuning reward functions. Let an autonomous agent iterate to convergence while you focus on what matters.