Hardware
Hysteresis
Hysteresis is the dependence of a system's output on its input history, producing different responses on loading versus unloading paths. In robots it appears as backlash and torsional wind-up in gearboxes, friction and stretch in tendon drives, and lag in sensors and elastomer-based tactile skins, introducing path-dependent errors that simple linear models cannot capture.
Why it matters for physical AI
Unmodeled hysteresis silently corrupts calibration, repeatability, and sim-to-real transfer, so identifying or learning these effects is often the difference between a policy working in simulation and on hardware.
Build physical AI
Put these concepts to work on real hardware
Axol is a dual-arm robot built for physical AI — teleoperate it, collect demonstrations, and deploy learned policies out of the box.