This paper presents a nonlinear controller based on a trajectory library. To generate the library, we combine two trajectory optimization methods: a parametric trajectory optimization method that finds coarse initial trajectories and Differential Dynamic Programming (DDP) that further refines these trajectories and generates linear local models of the optimal control laws. To construct a controller from these local models, we maintain the consistency of adjacent trajectories. To keep the resultant library a reasonable size and also satisfy performance requirements, the library is generated based on the controller's predicted performance. It is applied to standing balance control of humanoid robots that explicitly handle pushes. Most previous work assumes that pushes are impulsive. The proposed controller also handles continuous pushes that change with time. We compared our approach with a Linear Quadratic Regulator (LQR) gain scheduling controller using the same optimization criterion. The effectiveness of the proposed method is explored with simulation and experiments.

This paper presents a standing balance controller that explicitly handles pushes. We employ a library of optimal trajectories and the neighboring optimal control method to generate 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 further refine them and get their neighboring optimal control. A library generation method is proposed, which keeps the trajectory library to a reasonable size. We compare the proposed controller with an optimal controller and an LQR based gain scheduling controller using the same optimization criterion. Simulation results demonstrate the performance of the proposed method.

This paper addresses the basic behavior acquisition of the robot head based on the multisensor integration. A robot head generally has several degrees of freedom (D.O.F.) of motions as well as different kinds of sensors. The head motion is planned based on all sensors’ feedback. We take advantage of the Jacobian matrix to describe the differential relations between the sensor feedback and the motor motions. Hence, the relation between two sensors could be formulated by the two respective Jacobian matrices of both sensors to motors. Consequently, multisensor integration can be employed for better performance of the robot head. Experiments of basic behavior acquisition like gazing and head posture control are conducted. Performances of both basic behaviors of the robot head before and after multisensor integration are compared, which demonstrate that the proposed multisensor integration way improves the performance of control, robustness against some sensor’s failure, and reduces the overall computation.