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 "pre-conditions" 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 a balance, protective stepping when balance is disturbed, protective arm reactions when falling, and multiple ways of regaining an upright stance after a fall.