Convex Optimization Algorithms for Active Balancing of Humanoid Robots

Abstract

We show that a large class of active balancing problems for legged robots can be framed as a second-order cone programming (SOCP) problem, a convex optimization problem for which efficient and numerically robust algorithms exist. We describe this general SOCP balancing framework, show that several existing optimization-based balancing strategies reduce to special cases of this more general formulation, and investigate the computational performance of our SOCP algorithms through simulation studies involving a humanoid model.