Event Location: GHC 6115

Abstract: Humanoid robots represent the state of the art in complex robot systems, but the design of controllers can be challenging and tedious. 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 make analyzing and predicting these types of 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 strategies 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 converted into full-body joint torques using a technique known as Dynamic Balance Force Control (DBFC). Push recovery, walking and other force-based tasks are presented both in simulation and in experiments on the Sarcos Primus hydraulic humanoid robot.

Committee:Christopher Atkeson, Chair

Jessica Hodgins

Hartmut Geyer

Jerry Pratt, Institute for Human and Machine Cognition