Train the body
before the world.
Physara is a GPU-native physics simulation platform for humanoid robots. Generate richer physical experience, train policies against the edge cases that matter, and move promising behaviors toward real hardware with fewer physical trials.
Body-first simulation · Contact-rich dynamics · RL + imitation learning · Transfer evaluation
The numbers shown here are interface examples for the product demo, not production benchmarks.
Robots learn best when they can practise the hard parts safely.
Balance, hand control and recovery depend on physical experience. Real robot tests are valuable, but they are expensive, slow to repeat and difficult to vary.
Physara gives teams a place to practise those situations before they put another hour on a real machine.
Limited rigs mean fewer experiments, fewer failures observed and slower iteration.
Rare contact and recovery events are exactly the moments that are hardest to gather.
A policy that looks good in simulation still needs evidence that its behavior survives reality.
From physical state to repeatable skills.
Each capability answers a practical question: can the robot move, handle objects, recover from mistakes and repeat the skill when the conditions change?
Physical contact
Model the forces between feet, hands, objects and surfaces, with enough detail to understand what caused a movement to succeed or fail.
Inspect → Force / movement / slipHand and object control
Practise reaching, grasping, placing and reorienting objects across different shapes, surfaces and hand positions.
Train → Hand + object + surfaceRecovery testing
Add pushes, slips and terrain changes so the robot learns what to do when the original movement no longer works.
Stress → Disturbance / recoveryNew environments
Keep test environments separate so teams can see whether a skill still works somewhere new.
Check performance → Generalization / stabilityParallel practice
Run many variations of a task at the same time, giving the robot more practice before a hardware trial.
Scale → Environments / trialsReady for hardware
Keep a clear record of what worked, what failed and what is ready for controlled hardware testing.
Prepare for hardware → Policy / benchmark / traceFocus on the moments that cost the most hardware time.
Start with one difficult skill. Build more variety around it. Test the skill again before moving more work onto hardware.
Manipulation
Grasping, placing, opening, reorientation and dexterous contact across variable objects, surfaces and hand poses.
Best for → contact-rich skillsRecovery
Slips, pushes, imbalance and collision events where the right behavior starts after the nominal plan has already failed.
Best for → robustnessLocomotion
Uneven terrain, stairs, gait transitions and stabilization with changing friction and body-state conditions.
Best for → dynamic controlLarge-scale skill training
Run many related tasks and environments when a team needs a wider base of physical experience.
Best for → broader practiceBring us the task that keeps failing.
We are interested in the difficult cases: a grasp that changes with the object, a recovery movement that fails on a new surface, or a walking skill that struggles when the terrain changes.