Autonomous reactive control for simulated humanoids

We present a framework for composing motor controllers into autonomous composite reactive behaviors for bipedal robots and autonomous, physically-simulated humanoids. A key contribution of our composition framework is an explicit model of the “preconditions” under which motor controllers are expected to function properly. Pre-conditions may be determined manually or learned automatically by algorithms based on Support Vector Machine (SVM) learning theory. We demonstrate controller composition and evaluate our composition framework using a family of controllers capable of synthesizing basic actions such as balance, protective stepping when balance is disturbed, protective arm reactions when falling, and multiple ways of regaining an upright stance after a fall.