Current Research

Standing balance control is an important control problem for humanoid robots. It is shown that a single optimization criterion can be used to generate multiple balance recovery strategies. We employ a library of optimal trajectories and the neighboring optimal control method to compute local approximations to the optimal control. We take advantage of a parametric nonlinear optimization method, SNOPT, to generate initial trajectories and then use Differential Dynamic Programming (DDP) to refine them. A library generation method is proposed, which keeps the trajectory library to a reasonable size. We compare the proposed controller with an LQR based gain scheduled controller with the same optimization criterion. Simulation results demonstrate the performance of the proposed method.