for Force-Controlled Humanoid Robots

Abstract

Humanoid robots represent the state of the art in complex robot systems, but the design of high performance controllers is challenging. High performance controllers that can handle unknown perturbations will be required if complex robots are to one day interact safely with people in everyday environments. The high degree of freedom, dynamic and unstable nature of humanoid robots make analyzing and predicting full body behaviors difficult. This thesis demonstrates the use of simple models to approximate the dynamics and simplify the design of reactive balance controllers. These simple models define distinct balance recovery strate- gies and improve state estimation. Push Recovery Model Predictive Control (PR-MPC), an optimization-based reactive balance controller that considers future actions and constraints using a simple COM model, is presented. This controller outputs feasible controls which are realized by Dynamic Balance Force Control (DBFC), a force controller that produces full body joint torques. Push recovery, walking and other force-based tasks are presented both in simulation and in experiments on the Sarcos Primus hydraulic humanoid robot.