A simulation stack built for the way robots actually move.
Physara starts with the physical details that matter: body movement, contact, balance, hands, sensors and actuators. Everything else is built around them.
From a robot model to a testable skill.
The platform focuses on the parts that are hardest to reproduce on hardware: whole-body contact, dexterous hands, balance, collisions and recovery.
Body model
Articulated structure, inertial properties, sensors and actuator behavior.
Contact world
Surfaces, friction, collisions, terrain and randomized physical conditions.
Skill training
Train the same skill across many environments and conditions.
Stress testing
Test slips, pushes, balance loss and recovery before hardware trials.
Best behaviour
Keep the best behaviours ready for controlled hardware testing.
Practise the difficult moments before hardware.
Create difficult situations, see how the robot responds, improve the skill, and test it again. The point is simple: more useful practice before physical testing.
Build task
Scene + bodyChange the conditions
Surface + objectsPractise the skill
Repeated trialsTest weak points
Slips + balanceCheck performance
New environmentsPrepare for hardware
Best behaviourMeasure improvement where it matters.
The useful signal is not only success. It is whether the robot becomes more stable as conditions change.
Every trial tells you something.
A useful simulation should help a team understand what happened. Physara keeps the key events, movements and outcomes together so the next test is easier to plan.
Turn failed trials into better tests.
When a trial fails, teams can see the movement around the failure and decide what to change next: the skill, the environment or the robot model.
Simulation supports the move to hardware.
The simulator gives a skill room to improve. The policy is tested in controlled conditions, then the best result can move toward a real robot.
Experience
Generate many physical variations around the same task, including failures that would be expensive to gather by hand.
SimulationPolicy
Train, evaluate and compare candidate behaviors with the same telemetry and holdout scenarios.
LearningHardware
Move the best-performing policy toward controlled physical trials and feed the evidence back into the next simulation cycle.
Validation