This paper introduces a very efficient, modified resolved acceleration control algorithm for dynamic filtering and control of whole-body humanoid motion in response to upper-body task specifications. The dynamic filter is applicable for general upper-body motions when standing in place. It is characterized by modification of the commanded torso acceleration based on a geometric solution to produce a ZMP which is inside the support. The resulting feasible modified motion is synchronized to the reference motion when the computed ZMP for the reference motion again falls within the support. Contact forces at each foot are controlled through a dedicated force distribution module which optimizes the ankle roll and pitch torques. The proposed approach uses time-local information and is therefore targeted for online control. The effectiveness of the algorithm is demonstrated by means of simulated experiments on a model of the Honda humanoid robot ASIMO using a highly dynamic upper-body reference motion.

Resolved acceleration control is a well-known strategy used in tracking control of robotic systems where the desired motion is specified in task-space. Typically, such controllers are developed for systems which exhibit redundancy with respect to execution of operational tasks. While redundancy fundamentally adds new capabilities (self-motion and subtask performance capability), the degree to which secondary objectives can be faithfully executed cannot be determined in advance unless the motion is planned and the environment is known. Therefore, execution of secondary objectives cannot be guaranteed. In fact, a robot which exhibits redundancy with respect to operational tasks may have insufficient degrees of freedom to fulfill more critical objectives such as enforcing constraints. In this paper, we present a generalized constrained resolved acceleration control framework to handle execution of operational tasks and constraints for redundant and non-redundant task (and constraint) specifications. The approach is particularly well suited for online control of complex robot structures such as humanoid robots. The current formulation considers joint limit and collision constraints. The efficacy of the proposed algorithm is demonstrated by simulated experiments of task level upper-body human motion replication on the Honda humanoid robot.